analysis and interpretation of lower limb ... · emg activity was >75% for all muscles sites...
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Analysis and Interpretation of Lower Limb Mechanomyographic Signals in Human Gait
by
Katherine Plewa
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Institute of Biomaterials and Biomedical Engineering University of Toronto
© Copyright by Katherine Plewa 2018
ii
Abstract
Analysis and Interpretation of Lower Limb
Mechanomyographic Signals in Human Gait
Katherine Plewa
Doctor of Philosophy in Engineering
Institute of Biomaterials and Biomedical Engineering
University of Toronto
2018
This thesis investigates the patterns of accelerometer-based MMG in the lower extremities during
gait. To characterize dynamic muscle activity, we measured lower limb mechanomyography
(MMG) and electromyography (EMG) during over ground and treadmill locomotion in typically
developing youth and adults. First, MMG activity was validated against coincidental EMG activity
and detection parameters were optimized for each muscle using a particle swarm optimization
(PSO) algorithm. The mean balanced accuracy between MMG muscle activity and concurrent
EMG activity was >75% for all muscles sites except the gastrocnemius, where concurrent activity
was only observed about 55% of the gait cycle. These findings suggest that during dynamic
movements, electrical muscle activity is not directly followed by mechanical activity of the muscle
fascicles since some muscles, like the gastrocnemius, contract isometrically and lengthen in the
absence of electrical activity. To understand how mechanical activity is coordinated between the
lower limb muscles, mechanical synergies were then extracted using non-negative matrix
factorization (NMF) analysis and compared against neural synergies. For treadmill walking and
running, mechanical muscle activity yielded lower dimensional synergies than did corresponding
electrical activity. Furthermore, mechanical synergies contained overwhelming co-activity of
muscles, possibly reflecting the active and passive lengthening of muscle fascicles as joints move
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to complete a motor task. These synergies can be used to track therapy progression by analyzing
how mechanical patterns change with MMG biofeedback.
Based on MMG patterns observed during typical gait, a Smartphone application, GaitTool App,
was developed as an at-home gait therapy tool. MMG was recorded at the tibialis anterior and the
lateral gastrocnemius using wearable sensors that transmit packets of MMG data to an Android
device via Bluetooth. MMG data were analyzed for spatiotemporal features and triggered auditory
feedback in the form of a cadence-driven rhythmic auditory stimulus to allow for anticipatory
movement preparation and execution. The sonification of the alignment of MMG peaks provided
an aural cue to differentiate between typical and atypical gait patterns.
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Acknowledgments
I’m so lucky to have such awe-inspiring people in my life who have provided me with many rich
opportunities to learn and have enabled me to experience so much of what this wonderful world
has to offer. I never imagined that life would ever lead me to this point, but I’m excited with the
path I’ve chosen, for the failures I’ve endured and the successes I’ve triumphed, and all the
incredible people who have shared their stories with me along the way. What started as an
academic endeavor quickly turned into the toughest, most personal exploration I’ve ever done –
I’m truly grateful for all the lessons I’ve learned along the way and I’m proud of the person I’ve
become.
I’d like to extend my deepest thanks to my supervisor, Dr. Tom Chau, for serving as both a rock
and a guiding light on this, often tumultuous, journey. Thank you for listening to and understanding
my struggles, thank your patience when I didn’t think I had it in me to continue, and thank you for
building me up when I had little faith in myself and in MMG. Your vision for a future filled with
endless possibilities is infectious, and I’m honored to have been able to work with you. You have
taught me that there is no challenge too big, and no person too small to make a difference in the
world.
I’d like to thank my committee members: Dr. Virginia Wright, Dr. Kei Masani, Dr. Michael Thaut,
and Dr. Lee Bartel. Thank you for asking tough and thoughtful questions that extended me past
my comfort zone. Your expertise and your ideas have been critical over the years, and I’m grateful
to be able to collaborate with truly groundbreaking researchers. I hope that our paths will cross
again in the future!
Thank you to the Postdocs: Dr. Ali Samadani and Dr. Silvia Orlandi. To Ali – thank you for
pushing me to think more like an engineer, for challenging me to work on my programming, and
for always making me laugh. To Silvia, your hard work and perseverance has given me the spark
that I’ve been looking for – thank you for pushing through the hard times, and for giving me the
reality checks to keep me in line. You are both exceptional researchers with brains so full of
knowledge and I’m lucky to call you my mentors and my friends.
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Thank you to my summer students: Matthew Patel, Olivia Paserin, and Matthew Silverman. Thank
you for your hard work and contributions to my thesis work. I hope you learned just as much from
me as I learned from you.
Thank you to all my PRISM lab mates, I’ve had a wonderful time learning alongside such bright
minds. Thank you for enduring my MMG struggles along the way, for letting me have my “mom
moments,” and for showing me that I really suck at board games. Thank you, to Ka Lun Tam and
Pierre Duez, for your patience and ongoing Matlab support, I’d probably be still coding in a circle
without you. A special thanks to Marcela Correa Villada, for understanding how frustrating MMG
is, and for always having faith in me to keep looking for answers – we’re blessed that God brought
us together for this. Thank you to my dear Zahra Emami, for being such a brilliant mind, a brilliant
heart, and a brilliant soul. You are soft yet fierce, a unique combination of fire and ice, and I’m so
lucky for all the pep talks, all the cries, and for all the times that you helped me see the best in
others and in myself. Thank you to Dr. Helia Mohammadi, you are one of the toughest lady bosses
I know. And finally, my warmest thanks to Dr. Amanda Fleury – thank you for being my person
on this adventure. Thank you for enduring the highs and the lows and for growing with me, no
matter the distance – I’m so grateful to have such a wonderful and badass friend in my life.
Thank you to all my friends, especially Michelle Gu and Sophie Wang, who have been there for
me through the good, the bad, and the really bad times…Real talk, I don’t know what I would have
done without you, you are my anchors. Thank you for all the laughs and all the tears, and all the
liters of wine consumed along the way – I’m lucky to have found friends who love each other like
family.
I’d like to thank my parents – for teaching me about hard work through example, for showing me
what it means to work as a team, and for teaching me to never give up on myself. Thank you for
your unconditional support and love, and for always setting the bar a little higher – in school and
in life. Thank you for challenging me to be the best human I can be.
Finally, my deepest thanks to Pauly, thank you for serving as my daily inspiration and my
motivation. I don’t know if you know how much respect I have for you – you’re the one who
pushes the limits and doesn’t take no for an answer. Thank you for doing all that you do. This
one’s for you, kid.
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Table of Contents
Acknowledgments.......................................................................................................................... iv
Table of Contents ........................................................................................................................... vi
List of Abbreviations .......................................................................................................................x
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
Chapter 1 ..........................................................................................................................................1
Introduction .................................................................................................................................1
1.1 Motivation ............................................................................................................................1
1.2 Research Questions and Objectives .....................................................................................3
1.3 Thesis roadmap ....................................................................................................................4
Chapter 2 ..........................................................................................................................................6
A Novel Approach to Automatically Quantify the Level of Coincident Activity Between
EMG and MMG Signals .............................................................................................................6
2.1 Abstract ................................................................................................................................6
2.2 Introduction ..........................................................................................................................7
2.3 Methodology ........................................................................................................................8
2.3.1 EMG and MMG Signals ..........................................................................................8
2.3.2 Detecting Concurrent EMG and MMG Activity .....................................................9
2.3.3 Muscle-Specific Optimization of Intermodal Agreement ......................................11
2.3.4 K-fold Cross-Validation.........................................................................................12
2.3.5 Statistical Analysis .................................................................................................12
2.4 Results ................................................................................................................................13
2.4.1 Muscle-Specific Detection .....................................................................................14
2.5 Discussion ..........................................................................................................................15
2.5.1 Criterion-Driven Quantification of EMG and MMG Agreement ..........................15
vii
2.5.2 Need for Parameter Optimization ..........................................................................16
2.5.3 General Agreement Between MMG and EMG Activation During Gait ...............17
2.5.4 Discrepancies Between EMG and MMG for the Gastrocnemius Muscle .............18
2.6 Conclusions ........................................................................................................................20
Chapter 3 ........................................................................................................................................21
Comparing Electro- and Mechano-myographic Muscle Activation Patterns in Self-Paced
Pediatric Gait .............................................................................................................................21
3.1 Abstract ..............................................................................................................................21
3.2 Introduction ........................................................................................................................23
3.3 Methodology ......................................................................................................................24
3.3.1 Participants .............................................................................................................24
3.3.2 Instrumentation ......................................................................................................25
3.3.3 Data Collection ......................................................................................................26
3.3.4 Signal Processing ...................................................................................................26
3.3.5 Co-incident EMG-MMG Activity .........................................................................27
3.3.6 MMG Stride Characterization................................................................................28
3.4 Results ................................................................................................................................29
3.5 Discussion ..........................................................................................................................32
3.5.1 Coincident MMG and EMG Activity ....................................................................32
3.5.2 Discrepant MMG and EMG Activity ....................................................................33
3.5.3 Distribution of MMG Signal Power Over Gait Cycle ...........................................33
3.5.4 Differences Between MMG and EMG Signal Power Distribution Over the
Gait Cycle ..............................................................................................................34
3.5.5 Limitations and Future Work .................................................................................35
3.6 Conclusions ........................................................................................................................35
3.7 Acknowledgements ............................................................................................................36
Chapter 4 ........................................................................................................................................37
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Mechanical Synergies During Gait as Revealed Through Mechanomyography ......................37
4.1 Abstract ..............................................................................................................................37
4.2 Introduction ........................................................................................................................38
4.3 Methodology ......................................................................................................................39
4.3.1 Participants .............................................................................................................39
4.3.2 Data Collection and Instrumentation .....................................................................40
4.3.3 Experimental Setup ................................................................................................40
4.3.4 Data Pre-Processing ...............................................................................................40
4.3.5 Muscle Synergy Extraction ....................................................................................41
4.3.6 Walking vs. Running .............................................................................................42
4.4 Results ................................................................................................................................43
4.4.1 Electro-mechanical muscle activity in gait ............................................................43
4.4.2 Extracting Muscle Synergies .................................................................................44
4.4.3 Neural Synergies ....................................................................................................46
4.4.4 Mechanical Synergies ............................................................................................47
4.4.5 Walk vs. Run ..........................................................................................................49
4.4.6 Reconstruction of Muscle Signals .........................................................................50
4.5 Discussion ..........................................................................................................................52
4.5.1 Electromechanical Activity during Gait ................................................................52
4.5.2 Muscle Synergy Analysis ......................................................................................53
4.6 Conclusions ........................................................................................................................56
4.7 Acknowledgments..............................................................................................................56
Chapter 5 ........................................................................................................................................57
Designing a Wearable MMG-based Mobile App for Gait Rehab.............................................57
5.1 Abstract ..............................................................................................................................57
5.2 Introduction ........................................................................................................................58
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5.3 Mobile App Design ............................................................................................................59
5.3.1 Design Considerations ...........................................................................................60
5.3.2 MMG Muscle Activity ...........................................................................................61
5.3.3 Arduino Processing ................................................................................................62
5.3.4 Gait Analysis and Feature Extraction ....................................................................63
5.3.5 Auditory Biofeedback ............................................................................................63
5.3.6 Use Case.................................................................................................................64
5.4 Mobile App Implementation ..............................................................................................65
5.5 App Evaluation ..................................................................................................................66
5.6 Discussion and Conclusions ..............................................................................................66
5.7 Acknowledgments..............................................................................................................67
Chapter 6 ........................................................................................................................................68
Conclusions ...............................................................................................................................68
6.1 Summary of Contributions .................................................................................................68
6.2 Future Work .......................................................................................................................69
6.3 Publications ........................................................................................................................70
6.3.1 Journal Articles ......................................................................................................70
6.3.2 Conference Presentations .......................................................................................70
References ......................................................................................................................................72
x
List of Abbreviations
CNS Central nervous system
RAS Rhythmic auditory stimulus
CP Cerebral palsy
MMG Mechanomyography
EMG Electromyography
TA Tibialis anterior
LG Lateral gastrocnemius
MG Medial gastrocnemius
VL Vastus lateralis
BF Biceps femoris
TP True positive
FP False positive
TN True negative
FN False negative
𝜏 Amplitude threshold
𝜔 Moving window size (ms)
𝜔 Activity overlap (%)
BACC Balanced accuracy
PSO Particle swarm optimization
GLM General linear model
AMG Acoustic myography
RMS Root-mean-square
FSR Force sensitive resistor
MTC Musculotendinous complex
NMF Non-negative matrix factorization
VAF Variability accounted for
𝑀 Muscle activation pattern
𝑊 Muscle synergy weights
𝐶 Temporal activation coefficient
SW Slow walk (3 mph)
FW Fast walk (4 mph)
SR Slow run (5 mph)
FR Fast run (6 mph)
W Grouped Walk
R Grouped Run
W+R Global Walk + Run
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List of Tables
Table 3-1 Maximums co-incident activity between EMG-MMG signals by muscle and side (right
vs. left leg) as measured by maximum balanced accuracies (first row). The subsequent rows
report the corresponding optimal values of window sizes and amplitude thresholds. Values
shown are mean and standard deviation across all participants. ................................................... 29
xii
List of Figures
Figure 1-1 Thesis roadmap ............................................................................................................. 5
Figure 2-1 - Determining coincident activity of EMG (blue) and MMG (red line) signals using an
amplitude threshold (black dotted line) and moving window (depicted by the boxes). Examples
of true negative (TN), true positive (TP), false negative (FN) and false positive (FP) cases, along
with the three parameters, the amplitude threshold (τ), the moving window size (ω), and the
minimum percent of EMG-MMG activity overlap (δ), are illustrated. ....................................... 10
Figure 2-2 Boxplot of the averaged balanced accuracy across participants at each muscle for the
left (yellow) and right (blue) sides. The central mark denotes the median for each muscle, the
edges of the box indicate the 1st and 3rd quartiles, whereas the whiskers denote extremes in the
data and the ‘+’ symbols represent outliers. ................................................................................. 14
Figure 2-3 Mean optimized PSO parameters across participants - the amplitude threshold (τ), the
moving window size (ω), and the minimum percent of EMG-MMG activity overlap (δ) are
shown for each sensor on the left (yellow) and right (blue). The boxplot edges represent the 1st
and 3rd quartiles with the central line representing the median, and the whiskers denote extremes
with the ‘+’ showing outliers in the data. ..................................................................................... 15
Figure 2-4 Example of one participant's optimized segmentation results for the right tibialis
anterior showing detection results corresponding to: (a) true positive (TP), (b) true negative
(TN), and (c) false positive (FP). For this participant (P16) and sensor, we observed an
intermodal agreement of 96%. ...................................................................................................... 18
Figure 3-1 EMG and MMG sensors attached to the participants' muscles (TA = tibialis anterior,
LG = lateral gastrocnemius, VL = vastus lateralis, BF = biceps femoris) shown on the left leg,
and the backpack worn by the participant containing the MMG data board and tablet (right). ... 26
Figure 3-2 Determining coincident activity of EMG and MMG signals using an amplitude
threshold (black line) and moving window (box). Examples of true negative (TN), true positive
(TP), false negative (FN), and false positive (FP) cases are illustrated. ....................................... 28
xiii
Figure 3-3 Balanced accuracies (BACC) averaged across all participants, showing degree of
EMG and MMG signal alignment as a function of amplitude threshold (vertical axis) and
window size (horizontal axis). BACC closer to 1 indicates greater coincident activity between
EMG and MMG. ........................................................................................................................... 30
Figure 3-4 The typical activity patterns are shown for one participant (P28), showing the mean
(black) with standard deviation (red) for all muscles of the right leg. The gait cycle begins at heel
strike and swing phase typically begins at 60% of the gait cycle. ................................................ 31
Figure 3-5 Mean EMG (clear box) and MMG (shaded box) signal power across all participants
for the right (top row) and left (bottom row) legs. The asterisks (*) specify significant differences
between EMG and MMG power within an interval of the gait cycle division. ............................ 32
Figure 4-1 An example of EMG and MMG during one gait cycle from the right leg recorded
during each of the treadmill speeds (slow walk (SW), fast walk (FW), slow run (SR), and fast run
(FR)) for a representative participant (P10). ................................................................................. 44
Figure 4-2 Scree plot of overall VAF (across participants) for each synergy level. Each row of
plots corresponds to VAF values for one condition: slow walk (SW), fast walk (FW), slow run
(SR), fast run (FR), walk (W), run (R), and global walk + run (W+R). ..................................... 46
Figure 4-3 An example of extracted neural synergies for the grouped conditions for one
representative participant (P08). At each synergy level, we show the corresponding synergy
weights (W) at each muscle (left (blue) and right (red) sides) and the mean synergy coefficients
(C) that together account for at least 95% of the reconstructed muscle signals. .......................... 47
Figure 4-4 Mechanical synergies extracted for all conditions for a representative participant
(P08). Muscles are grouped together with left (blue) and right (red) bars. .................................. 49
Figure 4-5 Cosine similarity matrix of EMG (left) and MMG (right) synergy weights for grouped
walk vs. run conditions. EMG synergies show distinct patterns between synergy levels (syn1,
syn2, syn3), whereas MMG synergies show a lot of similarity between synergies and conditions
(more red areas). ........................................................................................................................... 50
xiv
Figure 4-6 - In the NMF analysis, each original muscle signal (dotted line) is reconstructed
(black line) based on the synergy weights and synergy coefficients (coloured lines) through the
gait cycle. Shown are the reconstructions for EMG for Global W+R for P01. ............................ 51
Figure 4-7 - Reconstructed MMG signals based on two synergy levels in the NMF analysis.
Shown is P01 based on the Global condition................................................................................ 52
Figure 5-1 System flow of GaitTool app showing the main components at both the user and
system levels: MMG muscle activity measurement (A), gait analysis and feature extraction (B),
and auditory biofeedback (C). ....................................................................................................... 60
Figure 5-2 MMG sensors taped directly onto the muscle bellies of the tibialis anterior (A) and
lateral gastrocnemius (B). In this initial prototype, the user is able to carry the Arduinos in his
pockets during gait. ....................................................................................................................... 61
Figure 5-3 Example of filtered MMG of TA (blue) and LG (orange) showing aligned peaks (left)
that create a harmony and misaligned peaks (right) that do not create a harmony. ...................... 62
Figure 5-4 Example of filtered MMG of TA (blue) and LG (orange) showing aligned peaks
(left) that create a harmony and misaligned peaks (right) that do not create a harmony. ............. 64
1
Chapter 1
Introduction
1.1 Motivation
Neurological lesions, located in the brain or spinal cord, are characterized by an impairment of the
central and peripheral nervous systems leading to sensory and motor dysfunction. Central effects
primarily result in spastic paralysis, of which there are five main deficits: overreaction to
stretching, selective motor control reduction, return to primitive locomotor patterns, muscle phase
changes, and altered proprioception [1]. The integration of sensorimotor information is needed for
functional movements [2], and motor control theories reinforce that human movements are
governed by centrally activated motor programs and modulated by sensory inputs [3]. Therefore,
measuring impaired muscles during gait may provide a pathway for identifying motor patterns
contributing to errors in atypical movements.
Gait is a complex task to learn, with both a voluntary and automatic process that require interaction
between neural networks in the central and peripheral nervous systems with the connecting
musculature [4-6]. Gait patterns have been shown to alter with aging and neuromuscular disease
[5, 7, 8] and with the use of assistive gait devices [9]. Therefore, many rehabilitation efforts focus
on the plasticity of the spinal cord and nervous system in order to enhance sensory and motor
function. Gait training has shown use-dependent plasticity leading to functional recovery of step
patterns, and suggesting that clinical interventions focus on relearning functional movements [3,
4]. Pediatric therapy has shown that even if children are not working on a meaningful functional
activity, the repetitive and concentrated practice may be playing a role in neural plasticity [10].
Biofeedback therapy provides active information about real-time physiologic responses to
facilitate the acquisition of voluntary control over those responses; in this way, biofeedback may
provide the sensory stimulation needed to modify the CNS to regain normal patterns of movement
[10, 11]. Currently, there is a paucity of research on biofeedback training in pediatric neurological
rehabilitation.
2
Neurological rehabilitation makes use of various treatment modalities, including techniques from
neurologic music therapy, which can promote motor learning in children with neurological deficits
through auditory entrainment [12, 13]. The link between auditory and motor systems is evident
when applying rhythmic entrainment to movement disorder rehabilitation – the firing rates of
motor neurons are triggered by music and entrained to the auditory rhythm, thus driving the motor
rhythm into different frequency levels [13, 14]. Studies providing fixed rhythmic auditory stimulus
(RAS) have shown improvements in gait patterns and stride parameters for patients with stroke,
Parkinson’s disorder, traumatic brain injury, and cerebral palsy [15-18]. In children with cerebral
palsy (CP), damage to the motor cortex disrupts normal processes for motor control thereby
affecting rhythmic movements. Studies have shown improvements to symmetry and stride rate
with both therapy-guided and self-guided RAS gait therapies in children with CP, suggesting the
need for at-home therapies [15]. Therefore, designing a gait intervention that is both wearable,
improves access to rehabilitation and the potential for increased quality of life, which encompasses
the child’s perception of their social, physical and emotional well-being that evolves through
development [19].
Mechanomyography (MMG) is a method for measuring muscle activation, typically using
accelerometers or microphones, and has recently been introduced as an effective biofeedback tool.
MMG is the mechanical equivalent to electromyography (EMG), and has been used to describe
motor control strategies, in terms of the number of active motor units and firing rates [20-24].
Moreover, presenting MMG biofeedback during computer work has been beneficial in reducing
muscle fatigue [25] and is more accurate than EMG at showing muscle recovery from low force
contractions [21, 26]. Additionally, the development of accelerometer-based wearable
technologies makes MMG an appealing low-cost modality for long-term gait monitoring [27].
However, MMG research has been limited in complex, dynamic motor tasks because MMG signals
are susceptible to motion artifact [28-30]. Although gait studies have incorporated MMG with
functional electrical stimulation [31-34], there have been no studies measuring the spatiotemporal
patterns of MMG during gait. This information may provide the missing afferent information
needed to enhance motor control and the restoration of healthy gait patterns. MMG has many
promising applications in rehabilitation [30, 35, 36], including motor recruitment and
neuroplasticity after an injury [37], assessing pain with movement [38], and clinically with the use
of assistive devices and prosthetics [39, 40].
3
The overall objective of this thesis was to study lower limb MMG signal behavior during gait and
subsequently exploit the identified patterns in the development of a novel gait therapy tool for
environments of daily living.
1.2 Research Questions and Objectives
To determine the feasibility of harnessing MMG as a viable biofeedback signal during self-paced
gait, the following research questions were asked:
1. How can MMG activity be automatically detected such that the alignment between MMG
and EMG recordings is maximized at each lower limb muscle during self-paced gait?
Additionally, are there muscle-specific differences in the tuning of detection parameters?
2. What are the differences in signal power between EMG and MMG signals over the gait
cycle? Specifically, are modality-specific (i.e., EMG vs. MMG) and muscle-specific
differences observed over the gait cycle?
3. What are the underlying coordinated patterns of MMG signals during specific movements,
such as walking and running? Specifically, how many levels of mechanical synergies are
required to coordinate the lower limb muscles, and are there differences in the mechanical
synergies for walking and running?
Through this exploration, features of the MMG signals will be identified in order to generate
an instantaneous auditory feedback in a way that aurally distinguishes between typical and
atypical gait patterns. This exploration leads to the final research question:
4. How can MMG signal features from two muscles be used to generate instantaneous
auditory feedback in a way that aurally distinguishes between temporally aligned and
misaligned muscle activities?
To answer these questions, the immediate objectives of this thesis were:
1. To develop a method to detect MMG activity concurrent with EMG activity during gait
across lower limb muscles
2. To characterize MMG spatiotemporal activity during self-paced gait.
4
3. To determine the coordination of MMG activity during walking and running gait
Using these findings, MMG spatiotemporal patterns were analyzed and
4. Developed into a prototype of a Smartphone application for at-home gait therapy that:
a. Presents users with live auditory biofeedback of their MMG activity
b. Tracks user’s MMG synergies as an indication of their therapy progress
1.3 Thesis roadmap
The organization of this thesis is summarized in Figure 1-1. To address the above objectives, two
studies were completed. The results of the first study are presented in Chapters 2 and 3 which focus
on objectives one and two, validating and characterizing MMG-based muscle activity from
multiple lower limb muscles against the gold standard of surface EMG. These data were collected
from typically developing youth during self-paced gait. The second study of this thesis is detailed
in Chapter 4. This chapter addresses the third objective of this thesis. These data were collected
from typically developed adults during treadmill walking and running. Chapter 5 presents the
development of the “GaitTool” Smartphone Application (the final objective of this work) based
on rhythmic auditory stimulation (RAS) and sonography of the MMG signals recorded in Study
1. Finally, the main contributions of this research and suggested areas for future work are
summarized in Chapter 6.
5
Figure 1-1 Thesis roadmap
6
Chapter 2
A Novel Approach to Automatically Quantify the Level of Coincident Activity Between EMG and MMG Signals
2.1 Abstract
Although previous studies have highlighted both similarities and differences between the timing
of electromyography (EMG) and mechanomyography (MMG) activities of muscles, there is no
method to systematically quantify the temporal alignment between corresponding EMG and MMG
signals. We propose a novel method to determine the level of coincident activity in quasi-periodic
MMG and EMG signals. The method optimizes 3 muscle-specific parameters: amplitude
threshold, window size and minimum percent of EMG and MMG overlap to maximize the
agreement (balanced accuracy) between electrical and mechanical signals. The method was
applied to bilaterally recorded EMG and MMG signals from 4 lower limb muscles per side of 25
pediatric participants during self-paced gait. Mean balanced accuracy exceeded 75% for all
muscles except the lateral gastrocnemius (LG), where EMG and MMG misalignment was notable
(56% balanced accuracy). The observed temporal discrepancy between EMG and MMG activities
of the LG muscle can be interpreted in terms of the energy-conserving interaction between the LG
muscle and tendon during the gait cycle, resulting in the nearly isometric contraction of the LG
during stance. The proposed method can be applied to the criterion-driven comparison of any two
sets of biomechanical signals.
7
2.2 Introduction
The determination of muscle contraction onset and offset during dynamic activities (i.e., those that
involve more than an isolated concentric or eccentric contraction) is useful for the assessment of
motor control and learning [41], tracking rehabilitation progress [42], training myoelectric control
of prostheses [27, 43, 44], and informing the design of robotics or access technologies [39, 45, 46].
Muscle activity can be revealed by mechanomyography (MMG), which is a method for measuring
the lateral oscillations of muscle fibres during contraction [41-43]. MMG is considered the
mechanical counterpart of electromyography (EMG), which is the conventional method for
measuring muscle activity [43]. As such, several kinesiological and clinical studies have deployed
MMG as a complementary signal to EMG in detecting neuromuscular pathologies and in
controlling multifunction access devices [42, 43, 47-49]. Additionally, bimodal systems with
EMG-MMG have been suggested for the identification of electromechanical efficiency in atrophic
or diseased muscle [50].
The identification of muscle activity is a precursory step to the study of contraction timing or
waveform morphology [51, 52]. Previous research has shown that the onset of MMG activity
generally corresponds to the onset of EMG activity in voluntary isometric contractions [53, 54].
However, it has also been recognized that the corresponding electrical-mechanical relationship
may not always be straightforward. In a previous work, we reported non-causal interactions
between electrical and mechanical activities, where mechanical activity may be present in the
absence of contemporaneous electrical activity and vice versa, during dynamic movements, such
as gait [55].
Given the non-trivial relationship between EMG and MMG signals, it is challenging to objectively
establish the level of their concurrence, especially in terms of the timing of muscle contractions
within a long recording of quasi-periodic activity such as walking. Many algorithms have been
proposed for the automatic detection of onsets and offsets of muscle activity from a single time
series, including methods based on amplitude thresholding [39], statistical modeling of activity vs.
rest [56], and signal feature classification [57]. However, it is not clear how these detection
methods could be extended to two simultaneously recorded time series, one EMG and the other
MMG, while affording a measure of their temporal agreement, in terms of concurrent
8
manifestations of muscle contractions. Simple cross-correlation is inadequate as the phase lag
between EMG and MMG is not necessarily static over time [55].
There is thus a need for a detection algorithm that can objectively quantify the concurrence
between MMG and EMG activities. Since MMG and EMG manifestations of disordered
movements may vary among individuals, the method also needs to be subject-specific. Here, we
propose a method to optimize the detection of MMG activity based on concurrent EMG activity
recordings. The proposed formulation deploys subject-specific window size, amplitude threshold,
and minimum EMG-MMG overlap to maximize the concurrence of detected EMG and MMG
activities at various lower limb muscles during self-paced gait. Furthermore, we compare detection
accuracy and optimized parameters across muscle sites to assess the need for muscle-specific
tuning of the detection parameters.
2.3 Methodology
2.3.1 EMG and MMG Signals
25 typically developing pediatric participants (7 males and 18 females; average height 159.8 cm ±
11 cm and average weight was 56.6 ± 17.6 kg) between the ages of 8 and 18 (mean 14 ± 3) years
were recruited. All participants provided written informed consent. The study protocol was
approved by the Research Ethics Boards of Holland Bloorview Kids Rehabilitation Hospital and
the University of Toronto. A subset of recordings from 20 of the above participants was previously
analyzed in [55].
Electrical and mechanical muscle activities were simultaneously measured from the muscle bellies
of the tibialis anterior, lateral gastrocnemius, vastus lateralis, biceps femoris muscles, bilaterally.
Sensors were attached to the skin with double-sided tape, with MMG sensors about 3 cm proximal
to EMG sensors. MMG was collected at 1 kHz using tri-axial accelerometers (ADXL337, Analog
Devices Inc, Norwood, MA) positioned so that the z-axis was perpendicular to the longitudinal
axis of the muscle. Surface EMG (Trigno by Delsys Inc, Boston, MA, USA) data were collected
wirelessly at 2 kHz. Participants walked continuously at a self-selected pace for 15 min, with shoes,
counter-clockwise around an indoor circuit (5m × 8m with 100-lb linoleum flooring).
9
Five non-overlapping 2-minute partitions of data were extracted from each participant’s recording,
for subsequent segmentation analyses. Partitions were selected visually to ensure continuous
walking (i.e., no stops) with no signal artifacts (i.e., unusually large signal deflections). The z-
component of the accelerometer signal was bandpass filtered (5th order Butterworth filter) between
5-50 Hz and full-wave rectified [29]. EMG signals were bandpass filtered (4th order Butterworth
filter) between 30-500 Hz, full-wave rectified, down-sampled to 1 kHz [51], and smoothed using
a moving window average of 101 samples. EMG and MMG signals were rendered zero-mean and
amplitudes were normalized from 0 to 1. All data analysis was carried out via a custom-designed
Matlab program.
2.3.2 Detecting Concurrent EMG and MMG Activity
The alignment between EMG and MMG signals was examined in two steps. The first was to
separately identify active regions within EMG and MMG signals. This procedure comprised
threshold-based activity detection over unity-normalized signals and involved tuning for the
normalized amplitude threshold-parameter, 𝜏. At any instant in time, a muscle was considered
“active” when the corresponding normalized signal exceeded its modality (MMG or EMG),
muscle and subject-specific amplitude threshold. Adjacent activity segments were merged together
if they were less than 10 ms apart, whereas segments that were less than 100 ms in duration were
discarded as they were likely non-physiological artifacts [39]. This first step yielded binary (1 or
0) indicator signals for MMG and EMG.
The second step was to evaluate the alignment of an MMG segment with its EMG counterpart
using balanced accuracy. A moving window of length, 𝜔 (ms), was used to divide the EMG and
MMG indicator signals into non-overlapping segments. The percent of overlap between EMG and
MMG segments was defined as the fraction of time in which both indicator segments were in the
ON state (value of 1). The percent of overlap was compared against a minimum overlap, (%),
to determine the appropriate label for the segment. A true positive segment was one where
concurrent EMG and MMG activity was identified whereas a window was labeled as false negative
when an active EMG segment occurred with no corresponding MMG activity. A true negative was
tallied when both EMG and MMG activities were absent within a window of time, while a false
positive was an instance of active MMG with no concurrent EMG activity (Figure 2-1).
10
Figure 2-1 - Determining coincident activity of EMG (blue) and MMG (red line) signals using
an amplitude threshold (black dotted line) and moving window (depicted by the boxes).
Examples of true negative (TN), true positive (TP), false negative (FN) and false positive (FP)
cases, along with the three parameters, the amplitude threshold (𝝉), the moving window size (𝝎),
and the minimum percent of EMG-MMG activity overlap (𝜹), are illustrated.
11
The definitions of true positives (𝑇𝑃), true negatives (𝑇𝑁), false positives (𝐹𝑃), and false
negatives (𝐹𝑁) can be succinctly expressed as follows:
𝑻𝑷 = ∑ 𝑰[𝑨𝒊 > 𝜹]𝒊 , 𝐰𝐡𝐞𝐫𝐞 𝑨𝒊 =𝟏
𝝎∑ 𝑰[𝑬𝑴𝑮𝒍 > 𝝉 ⋀ 𝑴𝑴𝑮𝒍 > 𝝉]𝝎
𝒍=𝟏 , (1)
𝑻𝑵 = ∑ 𝑰[𝑩𝒊 > 𝜹]𝒊 , 𝐰𝐡𝐞𝐫𝐞 𝑩𝒊 =𝟏
𝝎∑ 𝑰[𝑬𝑴𝑮𝒍 < 𝝉 ⋀ 𝑴𝑴𝑮𝒍 < 𝝉]𝝎
𝒍=𝟏 , (2)
𝑭𝑷 = ∑ 𝑰[𝑪𝒊 > 𝜹]𝒊 , 𝐰𝐡𝐞𝐫𝐞 𝑪𝒊 =𝟏
𝝎∑ 𝑰[𝑬𝑴𝑮𝒍 < 𝝉 ⋀ 𝑴𝑴𝑮𝒍 > 𝝉]𝝎
𝒍=𝟏 , (3)
𝑭𝑵 = ∑ 𝑰[𝑫𝒊 > 𝜹]𝒊 , 𝐰𝐡𝐞𝐫𝐞 𝑫𝒊 =𝟏
𝝎∑ 𝑰[𝑬𝑴𝑮𝒍 > 𝝉 ⋀ 𝑴𝑴𝑮𝒍 < 𝝉]𝝎
𝒍=𝟏 , (4)
where 𝐼[𝑃] is the Iverson bracket whose value is 1 if P is true and 0, otherwise. In the above
equations, the subscript 𝑖 denotes the 𝑖𝑡ℎ segment, and 𝐸𝑀𝐺𝑙 and 𝑀𝑀𝐺𝑙 indicate the 𝑙𝑡ℎ EMG and
MMG observations within the 𝑖𝑡ℎsegment of length 𝜔, respectively. The symbol ˄ denotes the
logical ‘and’ operation.
In summary, the amount of concurrent EMG and MMG activity was determined by three
parameters: the amplitude threshold (𝝉), the moving window size (𝝎), and the minimum percent
of EMG-MMG activity overlap (𝛿). These 3 parameters are depicted in
Figure 2-1.
2.3.3 Muscle-Specific Optimization of Intermodal Agreement
Particle swarm optimization (PSO) [58] was used to find a combination of participant-specific
segmentation parameters yielding the highest balanced accuracy in aligning EMG-MMG activity
at each sensor location. To identify muscle-specific concurrent MMG and EMG activity, we found
the normalized signal amplitude threshold (𝜏), the window size (𝜔), and minimum percent of
EMG-MMG activity overlap (𝛿) that together maximized the agreement between the active
regions of the two signals:
12
𝐚𝐫𝐠𝐦𝐚𝐱𝝉,𝝎,𝜹
𝑩𝑨𝑪𝑪 (𝑬𝑴𝑮, 𝑴𝑴𝑮|𝝉, 𝝎, 𝜹), (5)
where , , , and 𝐵𝐴𝐶𝐶 (𝐸𝑀𝐺, 𝑀𝑀𝐺|𝜏, 𝜔, 𝛿) is the
balanced accuracy of detecting concurrent EMG and MMG activity given the parameter set
{𝜏, 𝜔, 𝛿}. The balanced accuracy was defined as:
𝑩𝑨𝑪𝑪 = 𝟏
𝟐(𝑺𝒆𝒏𝒔𝒊𝒕𝒊𝒗𝒊𝒕𝒚 + 𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒊𝒕𝒚), (6)
where
𝑺𝒆𝒏𝒔𝒊𝒕𝒊𝒗𝒊𝒕𝒚 =𝑻𝑷
𝑻𝑷+𝑭𝑵, 𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒊𝒕𝒚 =
𝑻𝑵
𝑻𝑵+𝑭𝑷 (7)
The search for this optimal combination of parameters was performed over a discrete grid of
amplitude thresholds ([0.0, 0.15] in increments of 0.005), moving window sizes ([10, 1500] in
increments of 10), and minimum percent of EMG-MMG overlap ([0.7, 1.0] in increments of 0.05).
2.3.4 K-fold Cross-Validation
To evaluate the reliability of the optimized parameters for segmenting muscle contractions during
gait, a k-fold cross-validation was performed. In each fold, the segmentation parameters were
optimized using the training data (a 2-minute partition) and the resulting optimized parameters
were tested on the remaining four partitions. The overall performance of the proposed approach
was reported in terms of its average balanced accuracy over the test segments of the 5-fold cross-
validation.
2.3.5 Statistical Analysis
The distributions of average BACC were non-Gaussian according to the Shapiro Wilks Test for
normality and hence, non-parametric statistical tests were invoked. To test for a potential effect of
sensor location (i.e., muscle) on average balanced accuracy, a Kruskal-Wallis multivariate analysis
10 1500 0.7 1.0 0 0.15
13
of variance with Tukey- Kramer’s post hoc test was applied to average balance accuracy values
from each side (i.e., right and left) independently, with P<0.05 denoting a significant effect
(RStudio for R, Boston, USA).
A univariate generalized linear model (GLM) was then used to test for any effect of sensor location
with the interaction of the optimized parameters on balanced accuracy. The model used considered
the averaged balanced accuracy (BACC) as the dependent variable, the optimized parameters
(𝜏, 𝜔, 𝛿) as covariates, and, muscle and side as fixed factors. A Gamma (reciprocal) link function
was deployed. Significant effects were indicated by P<0.05 (RStudio for R, Boston, USA).
2.4 Results
The averaged balanced accuracy was greater than 75% at all sensor locations except at the
gastrocnemius (Figure 2-2). The gastrocnemius location had the lowest averaged balanced
accuracies at 48% (right) and 59% (left), whereas the vastus lateralis exhibited the highest
averaged balanced accuracies at 85% (right) and 83% (left). The Kruskal-Wallis test revealed a
significant effect of sensor location on mean balanced accuracy (p<0.001). Post hoc testing
identified the gastrocnemius sensor locations as having significantly lower balanced accuracy than
all other muscle locations on the right (p<0.001) and left (p<0.05) sides.
14
Figure 2-2 Boxplot of the averaged balanced accuracy across participants at each muscle for the
left (yellow) and right (blue) sides. The central mark denotes the median for each muscle, the
edges of the box indicate the 1st and 3rd quartiles, whereas the whiskers denote extremes in the
data and the ‘+’ symbols represent outliers.
2.4.1 Muscle-Specific Detection
The three optimized parameters for each participant and sensor location can be seen in Figure 2-
3. On average, the highest BACCs across all muscles were obtained with a combination of an
amplitude threshold in the neighbourhood of 0.11, a window size of approximately 440ms, and a
minimum EMG-MMG overlap of 85%. The GLM model that included the interaction among the
optimized parameters (𝜏, 𝜔, 𝛿) and the interaction between muscles and sides (muscles×sides) did
not identify any interaction effect of optimized parameters and side on BACC (p>0.05). However,
there was a significant interaction effect among the optimized parameters on BACC (p<0.001).
15
Figure 2-3 Mean optimized PSO parameters across participants - the amplitude threshold (𝝉), the
moving window size (𝝎), and the minimum percent of EMG-MMG activity overlap (𝜹) are
shown for each sensor on the left (yellow) and right (blue). The boxplot edges represent the 1st
and 3rd quartiles with the central line representing the median, and the whiskers denote extremes
with the ‘+’ showing outliers in the data.
2.5 Discussion
2.5.1 Criterion-Driven Quantification of EMG and MMG Agreement
We proposed a novel method for the automatic quantification of the level of coincident activity in
EMG and MMG signals. In our earlier work [21], we used an exhaustive search to detect the best
combination of window size ( ) and amplitude threshold ( ) at a fixed percent overlap of EMG
and MMG activity. With EMG as the reference, MMG-based muscular contractions were detected
in a single 2-min trial with balanced accuracies between 88% and 94% for all muscles except the
16
gastrocnemius. In this study, we formalized a criterion-driven, systematic approach to quantifying
EMG and MMG agreement while also allowing the minimum percent of EMG-MMG overlap to
vary. It is important to note that the proposed method is not limited to quantifying temporal overlap
of activity (Equation 5) but allows one to measure the agreement between signal modalities using
different criteria. For example, one could instead choose the agreement between spectral features
in EMG and MMG data as the optimization criterion. In such case, we might expect low BACC
across muscles as concentric and eccentric contractions are known to manifest in MMG spectra,
but not in EMG [59]. This flexibility allows researchers to systematically explore and objectively
quantify similarities and differences in EMG and MMG signals, as they pertain to clinically
relevant movements [60]. Moreover, the proposed method can be generally deployed in the
comparison of an unknown signal against a template signal (e.g., indicative of health). The
proposed method can thus indicate the degree to which an unknown signal departs from the
template [60]. In particular, comparing recorded MMG activity against normative templates using
the proposed approach may provide a quantitative assessment of passive elastic mechanisms
present in neurotypical walking [61].
2.5.2 Need for Parameter Optimization
Our method optimized spatiotemporal parameters for each muscle of each participant, for each
signal modality. Given that EMG and MMG signals were normalized within-modality, the
optimized threshold was thus effectively modality-specific. In isometric contractions, the
morphology of EMG amplitudes increase from baseline and plateau at maximum contraction, then
decrease back to baseline as the contraction ends [62]. In contrast, the typical MMG morphology
of an isometric contraction comprises a high peak-to-peak amplitude at the onset of contraction, a
plateau in amplitude during the isometric hold, and a lower peak-to-peak amplitude during the
relaxation phase of the contraction [63], suggesting the need for an amplitude threshold slightly
below the plateau value. Given these known morphological differences between EMG and MMG
signals for well-studied isometric contractions, modality-specific thresholds were invoked for
dynamic contractions of gait, where EMG-MMG correspondence is less studied.
A moving window between 370 ms and 530 ms was selected by the optimization algorithm for
concurrent assessment of electrical and mechanical activity during self-paced gait. If we consider
that the tibialis anterior is electrically active for about 50% of the gait cycle [64], the selected
17
window sizes are in line with previously reported gait parameters for school children, namely that
a typical stride rate is between 700 ms and 1000 ms [65]. Note that different window lengths may
be selected when considering other types of functional movements occurring on different time
scales. For example, typical lower limb muscle events during running are usually 50–100 ms in
duration, suggesting the use of shorter windows [66] to detect active MMG segments during gross
limb movement [29], or to identify participant-specific features in pathological movements, which
are often highly variable [67]. Limiting detection to the duration of physiological events of interest
through parameter optimization can also reduce false positives [8].
Generally, when using the proposed method to compare a measurement against a gold standard (in
this case EMG), detection parameters need to be set (through optimization) to hone in on the
differences (e.g., temporal, spectral, amplitude) of interest (specified by the criterion function).
2.5.3 General Agreement Between MMG and EMG Activation During Gait
At all but one muscle site, balanced accuracies exceeded 75%, suggesting general temporal
alignment of supra-threshold EMG and MMG signal segments. These results corroborate previous
work showing that electrical and mechanical activity of the lower limb muscles is predominantly
spatiotemporally aligned during self-paced gait [55]. As action potentials send electrical stimuli to
the motor units to contract (i.e., giving rise to EMG), muscle fascicles shorten or lengthen resulting
in corresponding mechanical activity (i.e., MMG signal) in the tibialis anterior, vastus lateralis,
and biceps femoris muscles [68]. Indeed, coincident EMG-MMG activity has been previously
reported for isometric and isokinetic contractions of upper and lower limb muscles [69-71], as well
as for dynamic contractions of the quadriceps during cycling [30]. In a gesture interaction system
[14], coincident EMG and accelerometer-based (mechanomyographic) activity was found to yield
high gestural onset and offset detection accuracy using a Hidden Markov Model. Our finding of
general temporal agreement between EMG and MMG activation in lower limb muscles during
walking thus resonates with previous literature. An example of optimized segmentation results
showing agreement and disagreement between EMG and MMG can be seen in Figure 2-4. For this
participant and sensor, the balanced accuracy was 96% and we present cases of true positive
segmentation, true negative, and false positive cases.
18
a) – TP
b) TN
c) FP
Figure 2-4 Example of one participant's optimized segmentation results for the right tibialis
anterior showing detection results corresponding to: (a) true positive (TP), (b) true negative
(TN), and (c) false positive (FP). For this participant (P16) and sensor, we observed an
intermodal agreement of 96%.
2.5.4 Discrepancies Between EMG and MMG for the Gastrocnemius Muscle
Contrary to the general trend, in the gastrocnemius, we found significantly lower (BACC of
approximately 55%) EMG-MMG agreement compared to that observed at other sensor locations.
This discrepancy indicates that during walking, the electrical and mechanical activities of the
gastrocnemius do not align for about half of the gait cycle. Although we previously identified a
similar discrepancy between EMG-MMG amplitudes at the gastrocnemius [55], this finding
departs from the general theme of electromechanical correspondence reported in the MMG
literature. For example, when examining EMG and acoustic myography (AMG) during stepping
exercises, Harrison et al. noted temporal alignment of electro-mechanical signals recorded from
the medial gastrocnemius [70], while Beck et al. reported electro-mechanical signal
correspondence in the quadriceps during cycling [30]. The discrepancy discovered in our study
suggests that the relationship between EMG-MMG activities may be more complex in gait. We
elaborate upon this relationship below, focusing on the gastrocnemius muscle.
Muscle architecture is important in determining a muscle’s mechanical function [72, 73].
19
Specifically, the lengthening capacity of a muscle is defined by the arrangement of the muscle
fibres and the sarcomeres. For example, the gastrocnemius is a bipennate muscle, which has shorter
muscle fibers, thus resulting in shorter fascicle displacements in comparison to longer-fibred
parallel muscles [73]. Furthermore, muscles with higher pennation angles and shorter fibre lengths
exhibit larger increases in muscle thickness during contraction [74]. Since MMG is reflective of
the initial length and volume changes of muscles during contraction [22], muscle architecture may
influence the pattern of mechanical activity observed during gait. When studying muscle fascicle
lengthening during the contact phase of human locomotion, Ishikawa, et al. [75] found a
discrepancy between fascicle shortening and low EMG activity of the soleus muscle during the
push-off phase of ground contact during gait. Interestingly, they did not observe the same
discrepancy between electrical activity and fascicle shortening of the medial gastrocnemius,
indicating that the pattern of fascicle length change is different between muscles [75]. Likewise,
our findings show that coincident EMG-MMG activity is muscle and function-specific. Thus,
generalizations about electrical and mechanical concordance should not be made between muscle
groups or functional movements.
Studies examining the behaviour of the muscle-tendon complex during gait report that the
gastrocnemius appears to contract nearly isometrically [76-78]. Given that MMG signal power is
known to be low during isometric contractions [25], one would thus expect minimal MMG activity
in the gastrocnemius while there is heightened EMG activity, which resonates with our observation
of poor temporal correspondence (~50%) between EMG and MMG activity of the gastrocnemius.
More specifically, Fukunaga, et al. [77] showed that the medial gastrocnemius muscle maintains
near-constant length while active during walking (contracts nearly isometrically), generating
minimal power with minimal energy cost. The tendon stretches during stance and recoils from the
beginning of single support to toe-off to create elastic strain energy. Part of this energy is released
by the tendon upon recoil, and is dissipated through the gastrocnemius during push-off. This
energy would contribute to some of the MMG vibrational activity observed after toe-off in swing
phase, and prior to heel strike.
Moreover, the gastrocnemius undergoes a concentric-eccentric contraction in the swing phase of
gait [79]. MMG has been shown to be more sensitive than EMG to the type of contraction being
performed. This is evident when observing the frequency spectra of eccentric versus concentric
contractions [59]. Specifically, MMG root-mean-square (RMS) amplitude is lower during
20
isometric than concentric and eccentric contractions, and electromechanical efficiency (MMG
RMS/EMG RMS) is highest during eccentric contraction [63]. Combined with the evolution of
contraction type over the gait cycle, electromechanical efficiency would help to explain the
observed MMG response. During early stance, a low level of EMG produces some MMG through
a concentric contraction associated with low electromechanical efficiency. During mid-stance
EMG increases, but MMG remains low as the muscle contracts isometrically with low
electromechanical efficiency. During the latter part of swing phase, EMG restarts causing a larger
MMG deflection through the eccentric contraction with the highest electromechanical efficiency.
Finally, MMG preceding this deflection may be attributable to the propagated vibration from
tendon recoil. In other words, it appears that the observed EMG-MMG discrepancy is principally
due to the fact that the LG muscle has a nearly isometric contraction period during stance.
Incidentally, the LG is minimally activated during quiet standing [80].
2.6 Conclusions
We have proposed a novel method for systematically quantifying the level of temporal alignment
between electrical and mechanical muscle activities from simultaneously recorded EMG and
MMG signals during pediatric gait. When applied to signals collected from 25 pediatric
participants, electro-mechanical alignment was observed in the tibialis anterior, vastus lateralis,
and biceps femoris but not in the lateral gastrocnemius. The observed temporal discrepancy
between EMG and MMG may be attributable in part to the unique behavior of the LG muscle-
tendon complex during the gait cycle and its corresponding time-varying electromechanical
efficiency. The proposed method can be extended to quantitatively compare any two sets of
biomechanical signals according to a defined criterion.
21
Chapter 3
Comparing Electro- and Mechano-myographic Muscle Activation Patterns in Self-Paced Pediatric Gait
The entirety of this chapter is reproduced from the following manuscript: Plewa, Katherine, Ali
Samadani, and Tom Chau. "Comparing electro-and mechano-myographic muscle activation
patterns in self-paced pediatric gait." Journal of Electromyography and Kinesiology 36 (2017):
73-80.
This is an author-created, un-copyedited version of an article published in the Journal of
Electromyography and Kinesiology. Elsevier B.V. is not responsible for any errors or omissions
in this version of the manuscript or any version derived from it.
© 2017. Elsevier B.V. http://dx.doi.org/10.1016/j.jelekin.2017.07.002
3.1 Abstract
Electromyography (EMG) is the standard modality for measuring muscle activity. However, the
convenience and availability of low-cost accelerometer-based wearables makes
mechanomyography (MMG) an increasingly attractive alternative modality for clinical
applications. Literature to date has demonstrated a strong association between EMG and MMG
temporal alignment in isometric and isokinetic contractions. However, the EMG-MMG
relationship has not been studied in gait. In this study, the concurrence of EMG- and MMG-
detected contractions in the tibialis anterior, lateral gastrocnemius, vastus lateralis, and biceps
femoris muscles were investigated in children during self-paced gait. Furthermore, the distribution
of signal power over the gait cycle was statistically compared between EMG-MMG modalities.
With EMG as the reference, muscular contractions were detected based on MMG with balanced
accuracies between 88-94% for all muscles except the gastrocnemius. MMG signal power differed
22
from that of EMG during certain phases of the gait cycle in all muscles except the biceps femoris.
These timing and power distribution differences between the two modalities may in part be related
to muscle fascicle length changes that are unique to muscle motion during gait. Our findings
suggest that the relationship between EMG and MMG appears to be more complex during gait
than in isometric and isokinetic contractions.
23
3.2 Introduction
Knowledge of muscle activity during dynamic activities such as gait is useful for the elucidation
of motor control and learning strategies [41], assessment of motor disabilities, monitoring the
progress of rehabilitation, development of neuroprosthesis [27, 43], and informing clinical
decision-making [42]. In particular, a comprehensive picture of muscle function can be derived
from continuous in situ recordings of muscle activity, as enabled through wearable technologies,
or “wearables.” Wearables consist of small, unobtrusive sensors attached to the body or to clothing
that monitor physiological and behavioral signals over extended periods of time. For example,
wearables utilizing movement sensors have been helpful in measuring gait dynamics, joint
kinematics, and the effectiveness of at-home rehabilitation efforts [27, 81]. Furthermore, these
smaller-sized wearables would be most useful in studying the smaller muscles in a pediatric
population.
Surface electromyography (EMG) records electrophysiological impulses over the muscle belly
during muscle contraction, and is the gold standard for measuring muscle activity [43]. EMG
applications historically include clinical diagnosis and rehabilitation monitoring, and more
recently, postural biofeedback, and control of human machine interfaces, such as communication
devices and video games [27, 82, 83]. An alternative to measuring the electrical impulses at the
onset of muscle activity is to measure the mechanical force transmission of muscle fibres via
mechanomyography (MMG). MMG measures the lateral oscillations of active and passive parts
of the series elastic component of the musculotendinous unit using various transducers, such as
microphones or accelerometers [41-43]. MMG has been used to describe motor control strategies,
in terms of motor unit summation [23], firing pattern during fatigue [21], and force during
contractions [63].
Some studies have explored the temporal relationship between EMG and MMG in voluntary
isometric and isokinetic dynamic contractions, reporting electromechanical delays between 20-
125 ms during voluntary contractions [84, 85]. Additionally, lower limb studies in cycle ergometry
have reported overlapping bursts of activity for simultaneously recorded EMG and MMG [53].
Consequently, a number of kinesiological and clinical studies have deployed MMG as a
complementary signal to EMG in assessing postural balance and age-related muscle changes [42,
43]. While EMG signals are not known to exhibit differences between concentric and eccentric
24
contractions, Jaskólska, et al. [86] reported contraction-specific MMG frequency and amplitude
responses in the upper extremity agonist and antagonist muscles. Perry-Rana, et al. [87] discovered
differences in the EMG and MMG responses among the vastus lateralis, rectus femoris and vastus
medialis during maximal eccentric contractions while in an earlier study Perry, et al. [88] hinted
at an association between the metabolic and MMG characteristics of muscular contraction.
Additionally, the relationship between EMG and MMG frequency spectral patterns is more
complex in voluntary than in electrically induced contractions [89, 90]. Collectively, these findings
suggest that the relationship between MMG and EMG signals during gait may not be
straightforward and that MMG may bear uniquely complementary information about the
underlying muscle activity. To our knowledge, the temporal patterns of lower limb MMG and their
correspondence to simultaneously recorded EMG activity have not been explored during gait.
The availability of accelerometer-based wearables makes MMG a feasible alternative to printed
electrodes for EMG wearables [91]. Furthermore, a systematic review examining the use of
wearable accelerometer-based technology for neurological populations in the community
highlighted the effectiveness of these technologies in distinguishing between typical and atypical
mobility patterns [81]. Additionally, the smaller size of the accelerometer-based MMG sensor is
particularly conducive to application in pediatric rehabilitation settings. Although some studies
have deployed MMG alongside EMG or functional electrical stimulation in neuromuscular
populations [32, 92, 93], there have been no studies specifically focusing on MMG patterns in
pediatric populations. In particular, the level of temporal concordance between EMG and MMG
signals of the lower limbs during self-paced gait in children remains unknown.
In this study, we measured MMG of the lower limb muscles simultaneously with EMG during
self-paced gait in a typically developing pediatric population to evaluate the temporal patterns of
MMG activity during the gait cycle relative to EMG, and gain insight into the corresponding
muscle-specific, accelerometer-based mechanical activity.
3.3 Methodology
3.3.1 Participants
We recruited 20 typically developing pediatric participants (5 males and 15 females) between the
ages of 8 and 18 (mean 14.3±3.2, range 8-18) years with an average height of 158.8±11.6 cm and
25
an average weight of 55.5±19.2 kilograms. This age group was chosen to ensure that participants’
gait patterns had developed into mature, adult-like patterns [94]. Participants reported no known
neurological disorders, or musculoskeletal injuries (current or previous) limiting their walking.
Participants wore athletic shoes and shorts during the study. The Holland Bloorview hospital and
University of Toronto Research Ethics Boards approved the study protocol, and all participants
provided informed written consent.
3.3.2 Instrumentation
Wireless surface electromyography (EMG) sensors (Trigno by Delsys Inc, Boston, MA, USA)
were fastened with double-sided tape over the muscle belly of each of the following lower limb
muscles: tibialis anterior (TA), lateral gastrocnemius (LG), vastus lateralis (VL), and biceps
femoris (BF). MMG data were collected using tri-axial accelerometers (ADXL337, Analog
Devices Inc, Norwood, MA). The accelerometers were positioned on the muscle bellies of the
same muscles, usually 3 cm proximal to the EMG sensor (Figure 3-1). For participants who were
less than 145 cm in height and under 33 kg in weight, we situated the accelerometer 2 cm from the
EMG sensor, to ensure proximity to the muscle belly and to avoid the corresponding tendon. For
each muscle, the accelerometer was oriented such that the z-axis was perpendicular to the
longitudinal axis of the muscle. An ultra-thin, force-sensitive resistor (FSR) (FSR 406, Interlink
Electronics, Shenzhen, China) was inserted inside the participant’s shoe to record heel strike for
each step [95]. All data were collected bilaterally.
Each sensor was connected to a data logging system housed inside a backpack (total mass: 1.83
kg) worn by the participant. All data were acquired via a custom-made LabVIEW program at a
sampling rate of 1 kHz for MMG and 2 kHz for EMG.
26
Figure 3-1 EMG and MMG sensors attached to the participants' muscles (TA = tibialis anterior,
LG = lateral gastrocnemius, VL = vastus lateralis, BF = biceps femoris) shown on the left leg,
and the backpack worn by the participant containing the MMG data board and tablet (right).
3.3.3 Data Collection
Prior to the walking trial, we collected 20 seconds of baseline MMG and EMG during quiet
standing. Participants were then instructed to walk continuously at a self-selected pace on an
obstacle-free, rectangular-shaped, well-lit indoor gym track (5 m × 8 m with linoleum flooring).
All participants walked counter-clockwise around the track continuously for 15-minutes.
Participants did not complain of fatigue or discomfort from wearing the sensors and backpack, and
all participants were able to complete the full 15-minutes of walking.
3.3.4 Signal Processing
To extract MMG content from the accelerometry signals, the z-component of the signal was
bandpass filtered (5th order Butterworth filter) between 5-50 Hz and full-wave rectified. EMG data
were bandpass filtered (4th order Butterworth filter) between 30-500 Hz, full-wave rectified, and
down-sampled to 1 kHz [51]. Both MMG and EMG signals were smoothed using a moving
window average of 101 samples and separately normalized to the interval [0,1]. All data analysis
27
was carried out via a custom-designed MATLAB program. We extracted 2 minutes of data starting
at the 2-minute mark of each participant’s 15 minute recording for subsequent EMG-MMG
temporal validation analyses.
3.3.5 Co-incident EMG-MMG Activity
The percent of overlap between EMG and MMG within a given window of observation was
defined as the fraction of time in which normalized versions of both signals exceeded an amplitude
threshold (described below), i.e., were “active”. Windows within which the overlap between MMG
and EMG active regions was at least 80% were identified as segments of co-incident activation.
To identify muscle-specific co-incident MMG and EMG activity, we found the window size and
the signal amplitude threshold that together maximized the agreement between the active regions
of the two signals. In other words, using EMG activity as the reference, we determined a window
length-amplitude threshold pairing that maximized the balanced accuracy of detecting MMG
activity. The exhaustive search for this optimal pairing was performed over a discrete grid of
amplitude thresholds (0.05 – 0.5 in increments of 0.05) and moving window sizes (100 – 1500 ms
in increments of 50). Balanced accuracy was defined as the arithmetic mean of sensitivity (true
positive rate) and specificity (true negative rate). A true positive occurred when MMG activity was
present given corresponding EMG activity whereas a false negative indicated an active EMG
segment with no corresponding MMG activity. A true negative was tallied when both EMG and
MMG activities were absent within a window of time, while a false positive was an instance of
active MMG with no concurrent EMG activity (Figure 3-2). Balanced accuracies were compared
across muscles with a one-way analysis-of-variance using RStudio for R (Boston, USA). One-way
analyses-of-variance were also invoked to evaluate across-muscle differences in window length
and amplitude thresholds.
28
Figure 3-2 Determining coincident activity of EMG and MMG signals using an amplitude
threshold (black line) and moving window (box). Examples of true negative (TN), true positive
(TP), false negative (FN), and false positive (FP) cases are illustrated.
3.3.6 MMG Stride Characterization
For the 15-minutes of self-paced gait, EMG and MMG signals were segmented based on the heel
strikes as identified in the FSR signals. Fifty steps from the beginning and end of the session were
discarded to account for familiarization and termination effects, respectively, and steps <400 ms
and >2000 ms were also discarded to account for missteps or stops during the trial. With this
procedure, no more than 15 steps were eliminated for a given participant. The length of each stride
was then normalized to 2 seconds, and the power of the EMG and MMG signals was separately
calculated within each 20% division of the gait cycle. Muscle-specific differences in signal power
between EMG and MMG signals over the gait cycle were assessed using a repeated measures two-
way analysis of variance with measurement modality (EMG; MMG) and gait phase (0-20; 20-40;
40-60; 60-80; 80-100%) as independent factors. Significant differences in power were further
investigated via post-hoc pairwise t-tests with a Bonferroni adjustment for multiple comparisons.
The statistical analysis was performed using RStudio. The above analyses (comparing balanced
accuracies across muscles and the effects of measurement modality and gait phase on signal power)
29
were replicated in subgroups of gender, age (8-12 years and 13-18 years) and body size (<60 kg
and >60kg).
3.4 Results
Balanced accuracies averaged across participants for different window lengths and amplitude
thresholds are shown in Figure 3-3. High accuracy suggesting strong coincidence between EMG
and MMG activities was seen for different amplitude threshold-window size pairings for the TA,
VL, and BF muscle sites. However, the maximum balanced accuracy for the LG muscle sites of
both right and left legs were significantly lower than that of all other muscle sites (p<0.001),
indicating low coincidence between MMG and EMG-demarcated activity. This finding is
corroborated when examining the maximum balanced accuracy achievable for each muscle on
each side, as shown in Table 1. No significant differences were seen among muscle sites for
window sizes (p =0.74) or threshold amplitudes (p = 0.48) for maximum balanced accuracies
across participants.
Table 3-1 Maximums co-incident activity between EMG-MMG signals by muscle and side
(right vs. left leg) as measured by maximum balanced accuracies (first row). The subsequent
rows report the corresponding optimal values of window sizes and amplitude thresholds. Values
shown are mean and standard deviation across all participants.
Right Left Right Left Right Left Right Left
Maximum
balanced accuracy0.94 ± 0.15 0.93 ± 0.14 0.61 ± 0.19 0.71 ± 0.21 0.93 ± 0.15 0.92 ± 0.14 0.89 ± 0.18 0.89 ± 0.18
Window size (ms) 414 ± 147 435 ± 138 408 ± 169 398 ± 141 394 ± 108 420 ± 110 405 ± 152 471 ± 167
Threshold (mV) 0.21 ± 0.11 0.23 ± 0.16 0.21 ± 0.13 0.16 ± 0.08 0.18 ± 0.11 0.17 ± 0.09 0.17 ± 0.11 0.20 ± 0.13
LateralGastrocnemiusTibialis Anterior VastusLateralis BicepsFemoris
30
Figure 3-3 Balanced accuracies (BACC) averaged across all participants, showing degree of
EMG and MMG signal alignment as a function of amplitude threshold (vertical axis) and
window size (horizontal axis). BACC closer to 1 indicates greater coincident activity between
EMG and MMG.
During the gait cycle (Figure 3-4), coincident activity between EMG and MMG for the TA, VL,
and BF was observed primarily after heel strike (0-20%) and through swing phase (60-100%).
Despite these similar patterns, there was a significant interaction between modality (EMG and
MMG) and gait cycle division for the TA, LG, and VL muscles bilaterally, and the BF for the left
side only (p<0.001), which indicates differences between EMG and MMG at different subintervals
of the gait cycle. For the TA muscle, subsequent paired t-tests for each division of the gait cycle
revealed significantly higher MMG than EMG power mid-cycle (p<0.001) and significantly lower
MMG at the end of gait cycle (p<0.05). This trend was more prominent on the right versus left
side (Figure 3-4). For the LG muscles bilaterally, we observed significantly higher MMG than
EMG power following heel strike and during swing phase (60-100%) (p<0.01), and significantly
lower MMG power mid-cycle where we observed the majority of the EMG activity (p<0.001).
Although the VL EMG and MMG patterns appeared similar for the given participant in Figure 3-
4, the mean EMG and MMG signal powers across participants differed mid-cycle for the right
31
side. However, for the left-sided VL, we observed significantly higher EMG activity at the
beginning of stance, and significantly higher MMG activity at the beginning of swing phase
(p<0.05) (Figure 3-5). A similar trend was seen between left-side VL and BF muscle; however,
the BF shows significantly higher MMG activity following heel strike (p<0.01). There was no
significant interaction between modality and gait cycle division, and there was no significant effect
of modality on power of the right-sided BF muscle only. Findings from the subgroup analyses by
gender, age and body size were consistent with those derived from the entire sample.
Figure 3-4 The typical activity patterns are shown for one participant (P28), showing the mean
(black) with standard deviation (red) for all muscles of the right leg. The gait cycle begins at heel
strike and swing phase typically begins at 60% of the gait cycle.
32
Figure 3-5 Mean EMG (clear box) and MMG (shaded box) signal power across all participants
for the right (top row) and left (bottom row) legs. The asterisks (*) specify significant differences
between EMG and MMG power within an interval of the gait cycle division.
3.5 Discussion
3.5.1 Coincident MMG and EMG Activity
When comparing the processed MMG and EMG for the shank and thigh agonist-antagonist
muscles, we observed bursts of MMG activity that corresponded to EMG activity. This MMG-
EMG correspondence was quantitatively verified by the high balanced accuracies for the TA, VL,
and BF muscles, and corroborates the qualitative reports of previous dynamic studies [88, 96]. For
example, in studying the relationship between leg extensor activity and work load during cycle
ergometry, Shinohara et al. remarked that the shapes of the rectified MMG “roughly
approximated” those of the EMG signals but with “some” time delay [96]. Additionally, Shinohara
et al. noted the presence of MMG signal activity during the “non-contraction phase” of the muscle
where no corresponding EMG was visible, attributing this discrepancy to vibrations propagating
from the antagonist muscles [96]. This antagonist muscle “noise” may in part account for the lower
balanced accuracies in our study. In studying the relationship between EMG, MMG, and exerted
power in incremental cycle ergometry, Perry, et al. [88] reported 10-seconds of raw EMG and
MMG activity, where it appears that MMG activity generally aligns with EMG activity, with
limited MMG activity between contractions. Although they did not discuss this relationship, they
33
did conclude that patterns of MMG amplitude may be more useful than EMG for examining power
output during continuous, dynamic tasks [88].
3.5.2 Discrepant MMG and EMG Activity
Interestingly, for the LG muscle, we found a large discrepancy between the EMG and MMG
signals. The accuracy of detecting active regions was only about 65% and the distribution of power
over the gait cycle was visibly different between modalities (Figure 3-4). The EMG power was
concentrated around the 50% mark of the gait cycle, i.e., when the shank is rolling over the foot
and the ankle is plantar flexing to maintain forward motion, which is the typical profile of EMG
activity during self-paced gait [1]. In our study, we observed a large peak in MMG power following
heel strike, then again around 60% of the gait cycle, and a third peak following toe off and before
heel strike (Figure 3-4). The observed MMG activity before and after heel strike has not been noted
in previous EMG/MMG studies [88, 96]. Further, the observed MMG-EMG temporal
misalignment is beyond the known electromechanical delay in voluntary, dynamic studies [53,
84]. The MMG signal for the LG muscle may in fact comprise vibrations from the passive
movement of surrounding non-muscular, soft tissues, and contraction of the antagonist muscles.
The latter is suggested by the peaks we observed in the MMG power of the LG at the end of swing
phase and after heel strike, corresponding to periods of electrical activity of the TA (Figure 3-5).
Previous research examining EMG and MMG during cycle ergometry concluded that some of the
MMG amplitude may be occurring from incomplete muscle relaxation and passive muscle fibre
movement [96], which may explain some of the discrepancies in our study between EMG and
MMG signal power during varying phases of the gait cycle. That agonist and antagonist co-
activation is manifested in MMG was also previously noted, albeit in an upper extremity study
following eccentric exercise, where the authors argued that such co-contraction provides joint
stability [97].
3.5.3 Distribution of MMG Signal Power Over Gait Cycle
MMG reflects the lateral oscillations of active and passive parts of the series elastic component of
the musculotendinous complex (MTC) and in particular, the muscle fibres that create mechanical
force [41-43]. Based on the force-velocity relationship, force is optimal at a certain muscle length
during isometric and slow concentric actions; however, that relationship is more complex in
dynamic actions [68]. The muscle-tendon complex is made up of the muscle fibres and the attached
34
tendons. Muscle fibres transmit force to and interact with the tendons given tendon compliance.
Therefore, it is important to understand the relationship between the lengths of both muscle fibre
and tendon during dynamic activities. In their measurements of fascicle length change and EMG
activity of the TA and VL during walking, Chleboun, et al. [68] found significant fascicle length
change and bursts of EMG activity in the TA between 60-80% of the gait cycle. Similarly, for the
TA muscle, we found an aggregation of MMG signal power during the first 20% of the gait cycle
and again around 60%. Furthermore, in the VL, Chleboun, et al. [68] observed significant length
change and EMG activity at the end of the gait cycle (75-100%); however, fascicle length did not
exhibit significant length change in the first or second portions of the gait cycle. Similarly, in our
study, we observed an accumulation of MMG signal power for the VL muscle in the initial and
final 20% of the gait cycle (Figures 3-4 and 3-5). Thus, our observations of MMG signal power
distribution over the gait cycle, can in part be understood in terms of underlying fascicle length
changes.
3.5.4 Differences Between MMG and EMG Signal Power Distribution Over the Gait Cycle
We found lower coincident EMG and MMG activation for the LG, and significant differences
between the modalities in the various stages of the gait cycle (Figure 3-5). Prior to the swing phase,
we observed substantial electrical activity as the gastrocnemius activated to assist with push-off.
In their study using ultrasonography to examine MTC during various dynamic movements,
Fukunaga, et al. [77] showed that during the stance phase of walking, the medial gastrocnemius
(MG) muscle fibres maintained constant length while the tendon stretched, whereas during push-
off, both MTC and tendon shortened rapidly. In our study, we saw high EMG power at the end of
stance phase (40-60%) with low corresponding MMG power (Figure 3-5). At this point of the gait
cycle, the gastrocnemius muscle is contracting nearly isometrically [77], where we typically see
lower peak-to-peak MMG amplitudes in comparison to concentric and eccentric components of
contraction [43, 63]. Thus, we should expect lower MMG activity during this period of electrical
activity. Furthermore, the Achilles tendon lengthens during stance and recoils in push-off [78],
potentially contributing to the MMG signal at these times. Additionally, we saw differences in
modality-specific power between left and right legs for the TA, VL and BF muscles, which may
be related to participants walking with the left leg always on the inside of the oval track. Although
we know that EMG muscle activity must be adjusted in order to make a turn during gait, overall
35
gait patterns remain unchanged [98]. Nevertheless, these findings suggest that the relationship
between EMG, MMG and the associated temporal delay may be more complex during dynamic
movements and future studies ought to consider muscle-specific MTC length changes in
interpreting lower limb MMG during gait.
3.5.5 Limitations and Future Work
MMG signal characteristics may vary between adjacent muscles, specifically medial versus lateral
gastrocnemius. Although some studies show comparable EMG activity between the LG and MG
heads when recording different speeds of walking and running [1, 99, 100], other studies focus
only on MG given its larger muscle belly and greater activation during self-paced walking [101].
Furthermore, differences in LG and MG muscle fibre architecture, such as muscle fascicle length,
their insertion angle, and muscle thickness, suggests that there may be differences in the MMG
signals of these two muscles [102, 103]. We did not see any effects of gender, age, or body size
on MMG power distribution and timing. However, future research should investigate potential
effects of age and body size on MMG frequency responses in dynamic contractions [86] in
developing and advanced aging populations. Future studies should also investigate MMG
differences between the MG and LG muscles as manifested during gait.
Despite the aforementioned inter-contraction noise, the average MMG signal for the TA muscle
revealed two distinct peaks corresponding to one EMG burst, one at the start and the other at the
end of the contraction (Figure 3-4). Beck, et al. [59] observed, via wavelet transform, MMG
spectral differences between the concentric and eccentric components of isokinetic contractions.
Thus, our finding of dual MMG peaks, may suggest that spectral filtering of MMG has potential
to differentiate between concentric and eccentric components of the contraction in gait. This would
be particularly important given that EMG spectral analysis does not distinguish between concentric
and eccentric contractions [63, 104, 105]. Wavelet-based MMG analysis for automatic detection
of concentric and eccentric contractions during gait thus merits further exploration.
3.6 Conclusions
During pediatric gait, electro- and mechano-myographic activation patterns appear to be
temporally aligned for the tibialis anterior, vastus lateralis and biceps femoris muscles of both legs.
36
However, for the lateral gastrocnemius muscle, the EMG and MMG activations exhibit large
temporal discrepancies, well beyond that attributable to electromechanical delay. Passive
vibrations of nearby tissues and vibrations of the antagonist muscles may contribute to the
observed EMG and MMG activation offsets at the LG muscle. Differences between EMG and
MMG signal power distributions over the gait cycle may be related to fascicle
elongation/shortening and muscle-specific musculotendinous complex length changes during gait.
The reported discrepancies between EMG and MMG temporal distributions of signal power
suggest a complementary role for MMG in identifying and tracking electro-mechanical changes
in musculotendinous behaviour due, for example, to injury, disease or training.
3.7 Acknowledgements
The authors would like to thank NSERC Create CARE program for funding the primary author,
and Ka Lun Tam and Pierre Duez from the Prism Lab for their assistance with instrumentation and
coding.
37
Chapter 4
Mechanical Synergies During Gait as Revealed Through Mechanomyography
4.1 Abstract
This paper investigates mechanical synergies during gait as revealed through lower limb
mechanomyography (MMG). We recruited 10 typically developed adults and recorded
simultaneous electromyography (EMG) and MMG of the tibialis anterior, medial and lateral
gastrocnemius, and vastus lateralis muscles during treadmill walking and running, each at 2
different speeds. Synergies were extracted from EMG and MMG signals during the gait cycle
using non-negative matrix factorization for each condition. On average, 2.49 ± 0.53 (VAF 96.60
± 0.81) synergies were extracted from EMG signals across all conditions and participants,
consistent with previous research. In contrast, only 1.70 ± 0.64 (VAF 95.95 ± 0.64) mechanical
synergies were extracted from the corresponding MMG signals across all conditions. Interestingly,
all extracted mechanical synergies captured muscle co-activation; however, there appear to be
distinct activation trends between walking and running conditions.
38
4.2 Introduction
Gait is a complex task involving both voluntary and automatic processes within the nervous and
musculoskeletal systems. To efficiently organize movements and actions, it is suggested that the
central nervous system (CNS) deploys simplifying neural commands called muscle synergies, to
efficiently control multiple muscles, rather than send separate commands to individual muscles
[4]. Muscle synergies provide a mechanism by which task-level motor intentions are translated
into detailed, low-level muscle activation patterns [106]. A small number of muscle synergies may
be invoked in varying combinations to produce a wide variety of motor behaviors [107].
When examining electromyography (EMG) data in gait, Ivanenko, et al. [100] showed that from a
sample of 25 muscles, gait could be explained by a combination of five basic synergies. Similarly,
other studies have shown that muscle synergies are motor task-specific, and variations in
movement, such as in reacting to a perturbation [106] or transitioning between walking and
running [108], are modulated by activation levels rather than activation patterns. However, muscle
synergies measured via EMG are limited because they only reveal the electrical, or neural aspect
of muscle function and do not convey the mechanical behavior of the muscles [43, 84, 109, 110],
which when paired with the electrical information can provide insight into sensory-motor
coordination [111]. Indeed, one can interpret the neural signal (EMG) as the input to the muscle
and the mechanical behaviour (MMG) as the output of the contraction [92]. A complementary
synergy analysis of the mechanical activity of muscles may thus be informative in studying
neuromotor control of movement [112] or its alteration with aging [113], the effect of different
exercise interventions on neuromuscular responses and force production [114], or neuromuscular
changes accompanying neurological disease and its mechanical consequences [92].
Mechanomyography (MMG) reflects the lateral gross movement of muscle fibres along with the
subsequent vibrations at the muscle’s resonant frequencies and the dimensional changes of active
muscle fibres [22, 24]. MMG is said to be the mechanical counterpart to EMG, and has been used
to describe motor control strategies, in terms of motor unit summation [10], firing pattern during
fatigue [11], and force generation during contractions [12]. Although MMG has been used as a
complementary modality to EMG in isometric and simple isokinetic contractions [32, 43], the
MMG response reflects both active and passive properties of muscle [115], which are in turn
affected by joint movement during dynamic motor tasks [22, 116]. Furthermore, our previous work
39
has shown that the relationship between EMG and MMG is more complex during dynamic
movements, giving rise to modality-specific activation patterns during the gait cycle [13]. Ting
and McKay [106] recommended that because of the interaction of musculoskeletal elements during
movement, neural commands and muscle actions should not be studied in isolation, but rather as
part of a neuromechanical model. To this end, it is important to consider the coordination of
mechanical muscle function.
Several studies have reported similar muscle synergies between walking and running as
determined through EMG [117-119]. Ivanenko, et al. [100] and Hagio, et al. [108], reported that
the neural control of both walking and running share 4 or 5 common synergies. These synergies
flexibly generate different movement patterns by modulating the weighting factors and synergy
coefficients associated with each synergy. To the best of our knowledge, there have been no
studies evaluating MMG-based muscle synergies. We thus set out to determine mechanical
synergies as manifested in mechanomyography during treadmill walking and running. Based on
mechanomyographic evidence of contemporaneous agonist and antagonist co-contraction [97,
116] and previously identified differences between EMG and MMG patterns during the gait cycle
[55], we expected that MMG-based synergies during gait may differ from those reported in EMG
research.
4.3 Methodology
4.3.1 Participants
We recruited 10 healthy adults (3 males and 7 females, 28 ± 9 years old, height: 165.5 ± 6.1 cm,
weight: 64.8 ± 12.9 kg). Participants reported no known neurological disorders, or musculoskeletal
injuries (current or previous) limiting their walking. Participants wore athletic shoes and shorts
during the study. The research ethics boards of Holland Bloorview Kids Rehabilitation Hospital
and University of Toronto approved the study protocol, and all participants provided informed
written consent.
40
4.3.2 Data Collection and Instrumentation
Wireless surface electromyography (EMG) sensors (Trigno by Delsys Inc, Boston, MA, USA)
were fastened with double-sided tape over the muscle belly of each of the following lower limb
muscles: tibialis anterior (TA), lateral gastrocnemius (LG), medial gastrocnemius (MG), and
vastus lateralis (VL). MMG data were collected using tri-axial accelerometers (ADXL337, Analog
Devices Inc, Norwood, MA). The accelerometers were positioned on the muscle bellies, usually 3
cm proximal to the EMG sensor, and attached with medical grade tape. For each muscle, the
accelerometer was oriented such that the z-axis was perpendicular to the longitudinal axis of the
muscle.
An ultra-thin, force-sensitive resistor (FSR) (FSR 406, Interlink Electronics, Shenzhen, China)
was inserted inside the participant’s shoe to record heel strike for each step [14]. All data were
collected bilaterally. Each sensor was connected to a data logging system housed inside a backpack
(total mass: 1.83 kg) worn by the participant. All analog data were acquired via a custom-made
LabVIEW program at a sampling rate of 1 kHz for MMG and FSR, and 2 kHz for EMG.
4.3.3 Experimental Setup
Prior to the walking trials, treadmill safety was reviewed with participants. A 30 second baseline
of quiet stance was collected. Participants were then given a 2-minute period to warm-up on the
treadmill (GK200T, GaitKeeper Rehab Treadmills, 2014 Mobility Research) at a self-selected
speed. Participants performed four 1-minute treadmill trials at the following speeds: 3, 4, 5, and 6
mph. These speed trials will be referred to as: 3 mph = slow walk (SW), 4 mph = fast walk (FW),
5 mph = slow run (SR), and 6 mph = fast run (FR), respectively. Participants rested for 30-60
seconds between trials as preferred, and completed a cool down period of a few minutes following
the last trial. Participants did not complain of fatigue or discomfort from wearing the sensors and
backpack, and all participants were able to complete all treadmill trials.
4.3.4 Data Pre-Processing
To extract MMG content from the accelerometry signals, the z-component of the signal was
bandpass filtered (5th order Butterworth filter) between 5-50 Hz and full-wave rectified [13]. EMG
data were bandpass filtered (4th order Butterworth filter) between 30-500 Hz, full-wave rectified,
41
and down-sampled to 1 kHz [15]. Both MMG and EMG signals were smoothed using a moving
window average of 101 samples. For each trial, heel strike was identified based on the FSR sensors
and used to demarcate the gait cycle. Each gait cycle was down sampled to 200 points.
For each condition, the signals from the first and last 10 gait cycles were removed to eliminate
initiation and termination effects. Subsequently, 15 strides from each leg were used to build a
matrix for synergy extraction; each matrix consisted of 8 rows (four muscles each for right and left
legs) x 3000 columns (15 gait cycles x 200 data points). The data in each matrix were amplitude
normalized by the maximum value across all 4 matrices (one for each condition) for each subject
so that all values ranged from 0 to 1. The data were further normalized by the within-matrix
standard deviation to have unit variance, thus ensuring that all muscles were equally weighted
[120]. All data analysis was carried out via custom-designed scripts and the NMF toolbox for
MATLAB [16].
4.3.5 Muscle Synergy Extraction
Synergies were extracted from seven different groupings of the data from the four conditions: Slow
Walk (3 mph), Fast Walk (4 mph), Walk (3 + 4 mph), Slow Run (5 mph), Fast Run (6 mph), Run
(5 + 6 mph), Global Walk + Run. For Walk and Run groups, the NMF matrix was created by
combining the slow walk and fast matrices together (8 x 6000 matrix), and the global walk + run
combined all four conditions (8 x 12000 matrix).
A non-negative matrix factorization (NMF) algorithm was used to extract muscle synergies for
each condition [121]. The technique assumes that a muscle activation pattern (M) is comprised of
a linear combination of a few muscle synergies recruited by a time- varying coefficient (C). The
recruitment coefficient represents the neural command that specifies how that synergy is
modulated over time, and how much each synergy contributes to a muscle’s total activity pattern
[4, 16-18]. Let W represent the muscle synergy matrix, with each column representing a muscle
synergy, and C be the synergy activation coefficients. Muscle activation pattern, M, can then be
expressed as,
42
𝑀𝑚×𝑛 = 𝑊𝑚×𝑠𝐶𝑠×𝑛 (1)
where m is the number of muscles (m = 8), n is the length of the gait cycle pattern (e.g., n = 3000
in the Slow Walk condition), and s is the number of muscle synergies. M, W and C are represented
as:
𝑀 = [𝑀1(𝑡)
⋮𝑀𝑚(𝑡)
], 𝑊 = [ 𝑊11 ⋯ 𝑊𝑠1
⋮ ⋱ ⋮𝑊1𝑚 ⋯ 𝑊𝑠𝑚
], 𝐶 = [𝐶1(𝑡)
⋮𝐶𝑠(𝑡)
] (2)
Using the calculated coefficients and weighting factors, a reconstructed muscle activation pattern
is given by:
𝑀𝑟 = 𝑊∗𝐶∗ (3)
where W* and C* represent synergy weighting factors and synergy activation coefficients.
Typically, synergies are retained such that and comprise a compact representation
of the original data.
The NMF was implemented using an iterative optimization. The matrices W and C, are randomly
initialized, and are iteratively updated such that the squared error between the original and
reconstructed data is minimized, i.e., . The goodness-of-fit between
reconstructed and original muscle signals was measured using the correlation of determination (r2)
and the muscle-specific and overall variability accounted for (VAF) at each condition. The number
of extracted synergies required to meet the threshold VAF will be termed the synergy level, .
4.3.6 Walking vs. Running
To examine the concordance between weighting factors among conditions, especially grouped
walking and running, we used a cosine similarity analysis [122]. When comparing two muscle
synergies, the inner product of the two muscle synergy vectors (divided by the product of their
norms) was compared between two conditions, representing the cosine of the angle between the
vectors. As such, an inner product closer to one represents greater similarity between the vectors,
thus similarity between synergies. A critical threshold of cosine similarity (r > 0.7682 ± 0.01)
s *W*C
2
,W Cmin M WC
43
was determined based on a Monte Carlo simulation of uniformly distributed random data with two
synergy levels that was performed on n = 1000 data sets [120, 123].
4.4 Results
All of the participants were able to perform both walking and running conditions, and all
participants spontaneously shifted their gait pattern from walking to running at 5 mph.
4.4.1 Electro-mechanical muscle activity in gait
EMG and MMG patterns for a representative participant can be seen in Figure 4-1. Overall during
walking conditions, we observed bursts of TA and VL electrical activity in the first 25% of the
gait cycle, and then activity ramped back up at the end of the gait cycle. A small peak of TA
activity was seen around 75% of the gait cycle. The MG and LG exhibited activity between 30 and
60% of the gait cycle. When examining the mechanical aspect, MMG activity for the TA and VL
muscles followed similar trends as EMG; bursts of activity occurred between initiation and the
first 25% of the gait cycle, followed by a small burst of activity around 50-70%, and an increase
in activity around 85%. The LG and MG exhibited similar MMG activity as the TA and VL at the
start and end of the gait cycle; however, more mechanical activity was observed between 55% and
75% of the gait cycle, especially of the LG.
During running, we observed that most of the EMG muscle activity shifted to the first 40% of the
gait cycle, except for the TA. EMG of the TA had two peaks, one around heel strike and a second
peak between 40 and 75% of the gait cycle. In terms of MMG, a similar trend of activity around
heel strike was observed; however, there were larger bursts of activity beginning at around 75%,
peaking around heel strike, then ending around 25% of the gait cycle. Similar to EMG patterns,
the second MMG peak tended to shift towards the first half of the gait cycle, and was associated
with a decrease in amplitude.
These results are in line with previously reported EMG data during walking and running [108, 120,
123], and similarly, MMG findings are comparable to previously recorded MMG during self-paced
gait in typically developing children [76, 99, 124].
44
Figure 4-1 An example of EMG and MMG during one gait cycle from the right leg recorded
during each of the treadmill speeds (slow walk (SW), fast walk (FW), slow run (SR), and fast run
(FR)) for a representative participant (P10).
4.4.2 Extracting Muscle Synergies
The reconstruction of neural and mechanical muscle signals via NMF analysis was assessed in
terms of VAF value [55]. The scree plot for the overall VAF is shown in Figure 4-2. For EMG,
the lowest VAF across all groups was 74%, whereas the lowest VAF for mechanical synergies was
91%. The VAF of the reconstructed EMG signals continued to increase with the inclusion of more
than two or three synergies, whereas with MMG signals, >90% variance is accounted for with only
45
one or two synergies. We extracted the lowest number of synergies based on an overall VAF cutoff
of 95% for both neural and mechanical data, as well as a minimum muscle VAF cutoff of 80% to
ensure that all muscles were well reconstructed in the overall synergy [121, 122].
Our EMG synergies show that across all conditions and participants, the average number of
extracted neural synergies was 2.49 ±0.81 at a mean VAF of 96.0 ±0.81%. We saw higher number
of synergies with walking (SW, FW) than running (SR, FR) conditions. In fact, on average, we
extracted 2.55 ±0.52 (VAF 96.46 ±0.72%) muscle synergies for walking (W) conditions, 2.27
±0.47 (VAF 96.32 ±0.79%) muscle synergies for running (R) conditions, and 2.73 ±0.47 (VAF
96.81 ±0.96%) for grouped walking and running (W+R) conditions, respectively. Across all
participants and groups, two or three synergies were extracted, and there was only one participant
in the FR condition where four synergies were extracted.
The average number of extracted mechanical synergies was 1.70 ± 0.64 at a mean VAF of 95.95
± 0.64% across all conditions and participants. Across grouped conditions, we extracted an average
of 1.55 ± 0.52 (VAF 95.99 ± 0.71) muscle synergies for walking (W), 1.91 ± 0.52 (VAF 95.97 ±
0.44) muscle synergies for running (R), and 1.82 ± 0.60 (VAF 95.88 ± 0.49) for grouped walking
and running (W+R). In most cases, one or two synergies were extracted across participants;
however, in seven cases, three mechanical synergies were extracted: one in SR, three in FR, two
in R and one in the global condition. For one participant, only one synergy was extracted across
all conditions – this synergy showed nearly full activity of all the muscles at once.
46
Figure 4-2 Scree plot of overall VAF (across participants) for each synergy level. Each row of
plots corresponds to VAF values for one condition: slow walk (SW), fast walk (FW), slow run
(SR), fast run (FR), walk (W), run (R), and global walk + run (W+R).
4.4.3 Neural Synergies
The extracted neural synergy weights and synergy coefficients for the grouped conditions are
shown for a representative participant in Figure 4-3. Across all conditions, we saw a consistent
pattern of medial and lateral gastrocnemius activity in Synergy 1, tibialis anterior activity in
Synergy 2, and vastus lateralis activity in Synergy 3 (Global W+R in Figure 4-3). For some
47
participants, synergy levels were switched or combined in certain conditions, a phenomenon
previously reported in the literature [117, 125]. For example, in the Run condition in Figure 4-3,
where Synergy 1 is made up of the TA-dominant synergy instead of the LG/MG synergy dominant
in the Global and Walk conditions. Similarly, the second and third synergies comprised co-activity
of LG/MG and VL activity; however, VL is still predominant in Synergy 2 and LG/MG are
predominant in Synergy 3. In cases where only two synergy levels were retained, the first synergy
was commonly composed of gastrocnemius and vastus activity, while the second synergy
consisted primarily of TA activity.
Figure 4-3 An example of extracted neural synergies for the grouped conditions for one
representative participant (P08). At each synergy level, we show the corresponding synergy
weights (W) at each muscle (left (blue) and right (red) sides) and the mean synergy coefficients
(C) that together account for at least 95% of the reconstructed muscle signals.
4.4.4 Mechanical Synergies
Mechanical synergies were extracted based on MMG activity and a minimum overall VAF set at
95%. At this threshold, between one and three synergies were extracted across all walking and
running conditions. When we look at the average VAF across all participants and conditions, two
synergies should be extracted for all conditions, which is represented for a typical participant
across all conditions in Figure 4-4. On average, Synergy 1 predominantly consisted of TA and VL
activity, whereas Synergy 2 involved more LG and MG activity. This trend was consistent across
48
all conditions for this participant, except grouped Walk, where we found more LG activity in
Synergy 2. Interestingly, there is a large burst of LG activity, while MG activity is only around
50% active, and the VL is active at around 80%. This is uncommon – typically, the LG and MG
are grouped together in one synergy, whereas the TA and VL are grouped together in the second
synergy. Similarly, the gastrocnemius muscles are commonly grouped together as they move
synergistically.
The Global synergies were extracted from all four grouped conditions (i.e., SW, FW, SR, FR), and
there was a dominance of TA and VL in the first synergy (Figure 4-4). Interestingly, in neural
synergies, we saw a dominance of LG and MG in the first synergy, and TA and VL in the second
synergy, which was reversed in MMG. When looking at the mechanical Global synergies, the TA
and VL synergists are more active in Synergy 1 and the LG and MG synergists are active in
Synergy 2. Interestingly, the Global synergy is more similar to the Run condition, suggesting that
there is a larger influence of Running synergies in the Global representation of mechanical activity.
When looking at the synergy coefficients across all conditions, there was a peak of activity
following heel strike (gait cycle 0%) and around 60% of the gait cycle in Synergy 1. The temporal
activity at this synergy is consistent across all conditions. In Synergy 2, there was a small peak
following heel strike, then a second peak near the end of the gait cycle. In walking conditions, the
second peak was closer to the end of the gait cycle (>90%), whereas in running, this peak of activity
spiked earlier in the gait cycle (around 75%). This is consistent with EMG that is activated earlier
in the gait cycle during running compared to walking [99]. In the global condition, the peak is
evident around 75% of the gait cycle, however the amplitude is visibly attenuated in comparison
to the individual walking and running trials. These differences may be indicative of kinematic
differences observed between walking and running.
49
Figure 4-4 Mechanical synergies extracted for all conditions for a representative participant (P08).
Muscles are grouped together with left (blue) and right (red) bars.
4.4.5 Walk vs. Run
Unlike EMG muscle synergies for walk and run conditions, extracted MMG synergies differed
between walking and running. In walking, we saw high LG (0.8) and MG (1.0) activity with only
about 0.25 activation of TA and VL each in the first synergy, and about 0.75 activation of TA and
VL, with about 0.3 activation of LG, in the second synergy. In contrast, in running, we see
predominantly TA, LG and MG activity in the first synergy, and high activity of LG in the second
synergy. Furthermore, the walk + run group did not appear as a combination of both synergy levels,
but rather as a more balanced pattern of predominantly high TA and VL activity in the first synergy
and high LG and MG in the second synergy.
50
When comparing Walking and Running conditions, EMG synergies show a distinct difference
within synergy levels in each condition (as depicted by blue areas in Figure 4-5; cosine similarity
lower than the critical threshold of r = 0.7682 ± 0.01) and high similarity between synergies
extracted from Walking and Running conditions (as depicted by red areas in Figure 4-5). In
contrast, the cosine similarity for MMG synergies showed high degrees of similarity across and
between synergy levels (cosine similarity greater than the critical threshold of r = 0.7682 ± 0.01).
Even though there is a great deal of similarity between and within conditions, there was also a
large amount of variability between participants and trials, as is evident with the lack of unifying
pattern in Figure 4-5.
Figure 4-5 Cosine similarity matrix of EMG (left) and MMG (right) synergy weights for grouped
walk vs. run conditions. EMG synergies show distinct patterns between synergy levels (syn1, syn2,
syn3), whereas MMG synergies show a lot of similarity between synergies and conditions (more
red areas).
4.4.6 Reconstruction of Muscle Signals
The original and reconstructed EMG and MMG muscle signals based on the Global W+R
condition for one representative participant are seen in Figures 4-6 and 4-7. On average, EMG
signals were reconstructed to about 85.07 ±9.78% VAF of the original signals; whereas MMG
51
signals were only reconstructed to about 81.38 ±8.11% VAF of the original signals across all
conditions and participants. A Shapiro-Wilk test for normality indicated that goodness-of-fit
showed a non-Gaussian distribution across conditions. Friedman’s non-parametric rank test with
Wilcoxon Signed-Rank post-hoc test for multiple comparisons (α = 0.05) was then applied for
block analysis of variance to determine if there was an effect of condition on goodness-of-fit for
mechanical and neural synergies across participants. All statistical analyses were performed using
RStudio for R (Version 1.0.136, RStudio, Inc.). For EMG signals, there was no effect of condition
on goodness-of-fit between reconstructed and original muscle signals (p=0.79). However, there
was a significant effect of condition on reconstruction error of MMG muscle signals (p<0.05).
Post-hoc tests showed significant differences in goodness-of-fit between Fast Walk and Fast Run
conditions (p=0.016) and Fast Run and Grouped Walk conditions (p=0.042).
Figure 4-6 - In the NMF analysis, each original muscle signal (dotted line) is reconstructed (black
line) based on the synergy weights and synergy coefficients (coloured lines) through the gait cycle.
Shown are the reconstructions for EMG for Global W+R for P01.
52
Figure 4-7 - Reconstructed MMG signals based on two synergy levels in the NMF analysis.
Shown is P01 based on the Global condition.
4.5 Discussion
This study presents an analysis of the spatiotemporal coordination of mechanical activity during
gait using non-negative matrix factorization decomposition. Understanding mechanical patterns
during motor tasks is useful in detecting pathology, providing feedback about movement patterns,
and in controlling assistive technologies and robots.
4.5.1 Electromechanical Activity during Gait
The spatiotemporal patterns of both mechanical and electrical aspects of muscle contraction are
comparable to previously reported data [55, 99, 109, 124]. Previously, in self-paced gait in youth,
we reported a discrepancy between gastrocnemius mechanical and electrical activity [55], which
is again observed in the present study. We proposed that this discrepancy between
electromechanical activities might be related to muscle architecture or gait maturation; however,
53
the current results maintain this observed discrepancy in both heads of the gastrocnemius in adult
participants. MMG demonstrates the length change of muscle fibres and the resulting vibrations
of the surrounding soft tissues during contraction [22, 23]. Additionally, studies have shown that
different length changes occur across different movements, and as such, the MMG reflects both
the active and passive length change in the contractile components [116]. Our results align with
fascicle length changes seen in gait in previous studies [68, 77, 78]. This reinforces our findings
that the MMG data reflect length changes of the muscle fibres during dynamic movements.
As gait transitions from walking to running, increases in speed are associated with an increase in
mechanical activity due to observed changes in kinematics, kinetics and muscle activity [126, 127].
During the gait cycle, we saw that EMG activity was dominant in the first half of the gait cycle
(Figure 4-1). However, we did not see this activity reflected in the measured MMG during running.
In fact, there was a decrease in MMG activity during the middle of the gait cycle (Figure 4-1).
When studying the muscle fascicle length changes of the triceps surae group, although the trends
were similar, there were differences between walking and running, suggesting fewer fascicle
length changes and more length changes in the tendinous complex during running [79, 128].
Additionally, as running speeds increase, we see less joint movement and less sarcomere length
changes [76], as well as a greater dependence on the elastic properties of the tendons to increase
movement efficiency [75], which may explain the decrease in MMG activity observed in our study.
As running speed increases, there is a greater dependence on the stretch-shortening cycle to
improve biomechanical efficiency [126].
4.5.2 Muscle Synergy Analysis
Most commonly, an NMF algorithm has been applied to decompose muscle activity and other
various large-scale data in neuroscience, computation biology, and image and audio processing
[129]. In this analysis, a high-dimensional data matrix is linearly decomposed into a low-
dimensional basis vectors and scaling coefficients that are more easily interpretable [129]. When
applied to surface EMG, the extracted muscle synergies provide a way of understanding how the
CNS organizes and recruits groups of muscles during various movements [107]. Previous research
has shown that walking and running can both be explained by common neural commands as
reflected in consistent muscle synergy patterns found in both children and adults [108, 125].
54
The neural synergies extracted in this study show results in line with previous work showing that
a few common synergies can be used to explain both walking and running gait patterns. Synergies
extracted from individual conditions were consistent with those synergies extracted from grouped
running or walking, as well as a global condition encompassing all gait patterns. This demonstrates
that the coordination of electrical activity measured through surface EMG is consistent between
movement patterns. In contrast to some other studies that extract four or five synergies [99, 100],
we extracted only two or three neural synergies. Since NMF analysis reflects the structure of the
data rather than the experimental design, the robustness of our synergies is limited by the input of
only four muscles. Some studies suggested that including more muscles in a synergy analysis
extraction might significantly affect the structure and number of muscle synergies [4, 20]. Despite
this, our results show common patterns in neural commands that direct the complex patterns of
muscle activity in dynamic movements. Although EMG is the common modality used to study
muscle activity, the limitation with this modality is that we cannot directly infer mechanical
properties of the muscles themselves.
Neuromechanical theories of motor control stress the importance of studying the neural input
alongside the mechanical output in order to better understand control of the whole movement. As
such, in this study, we proposed a synergy analysis that assesses the control and coordination of
mechanical activity as measured by accelerometer-based MMG. In comparison to neural
synergies, we extracted fewer dimensions of mechanical synergies, extracting only one or two
levels (>95% overall VAF) for most gait conditions and only three synergies in some cases (n=7).
When extracting two mechanical synergies, the weighting factors showed large amounts of co-
activity among muscles in each synergy (Figure 4-4), resulting in high degrees of similarity
between synergy levels (Figure 4-5). This is a departure from the pattern of neural synergies, which
pinpointed distinct groups of muscles at each synergy level, with minimal co-activation of other
muscles (Figure 4-5). This co-activity between synergies may be related to the concurrent
lengthening and shortening of muscle fibres during active and passive joint movement [130].
Several groups have studied the behavior of human muscles with live ultrasound during walking
and have observed the lengthening of muscle fascicles and the presence of EMG activity [68, 75-
78]. These studies showed that during gait, EMG activity may not be directly accompanied by
lengthening of the muscle fascicles – in fact, the gastrocnemius contracts isometrically and
lengthens in absence of electrical activity [77, 78], and similarly the tibialis anterior and vastus
55
lateralis show some periods of near isometric contraction [68]. If we consider that MMG measured
in complex, dynamic motor tasks to be related to the lengthening and shortening of muscle
fascicles, our MMG spatiotemporal patterns show peaks of activity that align with muscle fascicle
length changes during the gait cycle. Specifically, our recorded MMG data for walking and
running show a large peak of activity increasing before heel strike across all muscles, followed by
an MMG amplitude decrease subsequent to heel strike (Figure 4-1). This is in line with previous
work by Lichtwark, et al. [79], who showed that during walking, the gastrocnemius muscle fibres
maximally lengthen at heel strike, then shorten rapidly until the foot is flat on the ground surface.
At the same time, the ankle plantar flexes and the whole musculotendinous complex also shortens,
which may contribute to an increase in MMG amplitude. Another group studying the relationship
of MMG and joint angle during maximum voluntary contraction hypothesized that changes in
MMG amplitude may be related to the joint angle differences in mechanical properties of
contraction and/or slack in the muscle [131]. As such, MMG may reflect the combination of
muscle fascicle behavior and joint angle changes during gait, and since these movements are
occurring simultaneously at certain parts of the gait cycle, we can expect the coordination of
mechanical patterns between muscles to be similar. This is reflected in our results, where we see
higher goodness-of-fit between reconstructed and original signals at lower synergy levels in
comparison to neural signals.
Our findings suggest that a lower dimension of control than neural activity governs mechanical
muscle activity. This may in turn be related to the need for creating efficient movement patterns.
We know that the human body is designed to optimize the efficiency of movement. For example,
muscle and tendon interactions allow us to exploit the elastic properties and the stretch-shortening
cycle to increase the force generating capacity of muscle that is utilized in running [77]. Perhaps
the observed alignment of MMG responses across muscles during walking and running are
indicative of efficient movements where the muscle fascicles and joint angles move in unison.
Consequently, efficient movements should be represented by very low dimensional mechanical
synergies. Alternatively, movements that are inefficient, where muscle fascicle length changes
follow more atypical patterns, such as with spastic gait, mechanical synergies would be more
complex. Therefore, we may expect inefficient, pathological movement patterns to generate more
synergy levels than efficient, typical movement patterns. In this way, mechanical synergies could
be applied in clinical diagnostics, rehabilitation tracking, and in measuring athletic performance.
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4.6 Conclusions
This is the first study to analyze muscle synergy during walking and running incorporating both
neural and mechanical aspects of muscle function via EMG and MMG, respectively. Our findings
contribute to a fuller understanding of the neuromechanical control of selected lower limb muscles
during treadmill gait. EMG and MMG should be used as complementary modalities in research
since, together, they provide an understanding of the neural control and mechanical output of
muscle contraction, coordination, and movement. Future work should focus on investigating
mechanical synergies in populations with gait disturbances, and to expand the analysis to other
movements, such as reaching tasks.
4.7 Acknowledgments
The authors would like to thank NSERC Create CARE program for funding the primary author,
and Ka Lun Tam and Pierre Duez from the Prism Lab for their assistance with instrumentation and
coding.
57
Chapter 5
Designing a Wearable MMG-based Mobile App for Gait Rehab
The entirety of this chapter is reproduced from the following manuscript: K. Plewa, M. Silverman,
S. Orlandi, T. Chau, M.H. Thaut, Designing a wearable MMG-based mobile app for gait rehab,
in: IEEE Life Sciences, IEEE Xplore, Sydney, Australia, 2017.
This is an author-created, un-copyedited version of an article published in in IEEE Xplore. IEEE
is not responsible for any errors or omissions in this version of the manuscript or any version
derived from it.
© 2017 IEEE DOI 10.1109/LSC.2017.8268187
5.1 Abstract
Movement disorder therapies involving sonography and rhythmic entrainment have shown lasting
improvements to gait dynamics. Although optimal parameters for gait training have yet to be
defined, past studies have shown that increasing training frequency enhances neural
reorganization, thus supporting the development of wearable technologies in gait rehabilitation.
This paper presents a novel tool for the acquisition of muscle activity, their analysis, and
presentation as a live biofeedback signal that distinguishes between typical and atypical gait
patterns. Muscle activity is recorded and analyzed on an Arduino, then sent to an Android for
feature detection via Bluetooth. Auditory feedback will be presented as a fixed tempo based on
stride rate and an interactive drum kit based on matching gait patterns. By developing a tool that
can be used at-home, users will be able to train daily and maintain longer rehabilitation programs,
thus encouraging neural reorganization. This mobile app will allow us to improve quality of life
by enhancing training outcomes and functional gait dynamics.
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5.2 Introduction
Neurological rehabilitation makes use of various treatment modalities, including techniques from
neurologic music therapy, which has shown very positive impacts on learning and motor abilities
of children with neurological deficits [12]. The link between auditory and motor systems is evident
in the research, and its influences can be seen clearly when applying rhythmic entrainment to
movement disorder rehabilitation [14]. Studies providing fixed rhythmic auditory stimulus (RAS)
have shown improvements in gait patterns and stride parameters for patients with stroke,
Parkinson’s disorder, traumatic brain injury, and cerebral palsy [15-18]. In children with cerebral
palsy (CP), damage to the motor cortex disrupts normal processes for motor control thereby
affecting rhythmic movements. Studies have shown improvements to symmetry and stride rate
with both therapy-guided and self-guided RAS gait therapies in children with CP, suggesting the
need for at-home therapies [15]. Therefore, designing a gait intervention that is both wearable and
mobile accessible, improves access to rehabilitation and the potential for increased quality of life.
The study of muscle activity is important for a variety of clinical and rehabilitation applications,
including pathology diagnostics, prosthetic control, access technologies, and movement
biofeedback. Muscle activity can be measured using mechanomyography (MMG), a
complementary modality to electromyography (EMG), which describes the mechanical aspects of
muscle function based on the gross lateral movements of muscle fibers and the subsequent resonant
vibrations of the fibers and surrounding soft tissues [22, 35]. MMG has been used to describe
motor control strategies, force generation capabilities, to provide biofeedback, and control access
technologies. In some studies looking at optimizing muscle activity, participants showed more
ideal responses when presented with MMG than with EMG biofeedback [25, 133]. Alternatively,
muscle activity can be measured and MMG onset and offset can be used to control a musical
instrument [134, 135]. These studies support the development of MMG into interactive tools that
can be implemented into rehabilitation.
In the literature, we find several groups using mutual entrainment and interactive RAS for gait
therapy [17, 136]. Mutual entrainment occurs when a synchronized gait pattern is formed between
the user and the therapist, in this case, the “WalkMate” robot. Originally developed by Miyake
[136], “WalkMate” is an interactive auditory-cuing robot that measures and alters heel strike
auditory patterns to improve acute fractal dynamics of pathological gait. In their system, rhythmic
59
sounds corresponding to the timing of footsteps are exchanged between the user and the Walk
Mate robot, showing that both rhythms adapt mutually after to each other and a stable
synchronization is automatically generated. The hierarchical control of this nonlinear model is
divided into two modules that link sensory input and motor output. The first module generates a
walking rhythm based on an artificial rhythm generator and the user’s rhythm, and then defines
step timing. This rhythmic cue embeds the auditory information into a predictable rhythm, which
allows for anticipatory movement preparation and execution. The second module adjusts the
timing difference between the sensory input and the motor output in order to converge the two
rhythms towards a target phase. This type of interactive RAS has shown short-term lasting effects
on gait patterns after a six-week period of gait training [17, 136].
Despite improvements to gait, one of the limitations with these interventions is that they are
performed in a laboratory setting, which creates a need for more accessibility in outside settings.
Additionally, the recent influx of focus on wearable technologies in community based settings
highlights the need for the integration of wearable systems into rehabilitation.
In this paper, we present the design, implementation and evaluation of a novel MMG-based
biofeedback tool, GaitTool App, for mobile phone and Smartphone application. MMG temporal
features are used to distinguish between typical and atypical gait patterns and define harmonized
musical feedback. This app will allows us to provide a rehab intervention that is easily incorporated
into daily life, while studying the long term effects of this type of training program on gait patterns
and ultimately, neural reorganization.
5.3 Mobile App Design
The core GaitTool App is based on an experimental protocol for gait intervention, MMG muscle
activity detection, Arduino processing, and gait analysis algorithms. The GaitTool App
architecture is shown below in Figure 5-1.
The fundamental framework of the mobile application is based on: a. measurement of muscle
activity via MMG; b. temporal feature detection; and c. providing users with an auditory cue during
gait.
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Figure 5-1 System flow of GaitTool app showing the main components at both the user and system
levels: MMG muscle activity measurement (A), gait analysis and feature extraction (B), and
auditory biofeedback (C).
5.3.1 Design Considerations
Intuitive Auditory Biofeedback – convert real-time MMG data to an intuitive auditory output
that distinguishes between typical and atypical gait.
User-Specific Pattern Detection – MMG features are selected based on user’s gait abilities.
Muscle Function Monitoring – tracking MMG to monitor short- and long-term changes in
muscle activity patterns and gait dynamics.
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5.3.2 MMG Muscle Activity
MMG is acquired at a sampling frequency of 1 kHz with two tri-axial accelerometers (ADXL337,
Analog Devices Inc, Norwood, MA) positioned over the tibialis anterior (TA) and lateral
gastrocnemius (LG) muscle bellies, bilaterally (Figure 5-2). Accelerometers were taped directly
onto the skin, and wired to an Arduino UNO MCU board (ATmega328-based, Arduino) with
ribbon cables.
Figure 5-2 MMG sensors taped directly onto the muscle bellies of the tibialis anterior (A) and
lateral gastrocnemius (B). In this initial prototype, the user is able to carry the Arduinos in his
pockets during gait.
To extract MMG, the z-component of the accelerometer data is filtered between 5 to 50 Hz (2nd
order Butterworth) and squared. The mean of 150 squared samples is then low-pass filtered at 5
Hz (3rd order Butterworth).
62
Figure 5-3 Example of filtered MMG of TA (blue) and LG (orange) showing aligned peaks (left)
that create a harmony and misaligned peaks (right) that do not create a harmony.
5.3.3 Arduino Processing
This tool processes accelerometer data in a two-stage process. All Arduino code is written in the
C programming language. First, for every millisecond, accelerometer data is read into a 5-wide
circular buffer (b0). The buffer is passed through a filter (previously described in section ii) whose
value is stored in another 5-wide circular buffer (b1). The square of the newest value in b1 is added
to the running sum of the current 15-wide window (newMS).
Secondly, for every 15 milliseconds, the value of newMS is stored in the next index of a 10-wide
buffer of old MS values (b2), and the newMS is added to the first value of a 4-wide mean squared
buffer (b3). Once all 10 positions of b2 have been filled, the first value of b3 is set to be the mean
of the 150 collected samples by dividing by 150. This initialization period allows the system to
stabilize and ensure a consistent relation with the sensors.
After initialization, the next value in b3 is set to be the mean of the 9 last 15-sample blocks of data
from b2 plus the latest newMS value. The oldest value in b2 is then replaced with newMS. Then,
b3 is passed through a 3rd order Butterworth low-pass filter (5 Hz cutoff) and then the filtered data
63
is stored in a new 4-wide buffer (b4). A two-way Bluetooth connection between the mobile app
and the BLE Mini devices is established using RedBearLab’s Bluetooth library (Red Bear
Company Limited, 2015). Bluetooth is handled by a GATT service that will run on the mobile
device, and its receiver will listen for new data from either leg. Pre-processed MMG data is
transmitted to the app every 15 ms for further analysis.
5.3.4 Gait Analysis and Feature Extraction
This tool provides rhythmic entrainment based on the temporal patterns of MMG muscle activity.
The app was developed in Android Studio (Creative Commons Attribution 2.5), using the Java
programming language.
The custom code begins by analyzing melody contours of TA and LG MMG, and defining
temporal differences between muscle peaks. Positive sounds are played when muscle peaks align,
and negative sounds are played when muscle peaks do not align (Figure 5-3). Due to the pre-
processing done by the Arduino, peaks are smooth and easily detectable on the mobile device.
Alignment is defined as when the peaks from each muscle occur within 50 ms of each other. If the
peaks are more than this but less than 250 ms apart, this is defined as misalignment. If the time
difference is any more than 250 ms, the peaks are not considered to be related, i.e., part of a
different gait cycle.
Currently, this tool compares users’ MMG against typical patterns seen in the youth and adult
MMG during self-paced gait [55]. Based on previous clinical and therapy observations, children
and adults with neurological movement disorders have variable gait patterns, which are difficult
to classify across participants [118, 137]. As such, user gait needs to be pre-analyzed and gait
features need to be pre-defined by a therapist or clinician prior to at-home use. MMG gait data will
be stored on the tablet for further long-term analysis on the effectiveness of this tool for improving
gait patterns.
5.3.5 Auditory Biofeedback
A custom drum kit was developed in PureData (Pd, puredata.info, hosted by IEM) to provide a
realistic musical cuing system. The Pd for Android library allows the app to communicate with the
Pd software. Four kits and four sounds were developed based on various musical styles and
64
instruments: rock, funk, samba, 808, trombone, quacks, etc. These libraries can be personalized
for user preference.
Upon app startup, sound libraries to be played as auditory biofeedback will be selected by the user
(Figure 5-4). Auditory biofeedback will be presented to the user as a melody made up of a fixed
RAS based on the step sounds and an interactive RAS based on the drum kit layers.
Figure 5-4 Example of filtered MMG of TA (blue) and LG (orange) showing aligned peaks (left)
that create a harmony and misaligned peaks (right) that do not create a harmony.
5.3.6 Use Case
The app is designed to be intuitive and user-friendly. On startup, the user selects the desired drum
kit and sound library to move with. A switch may be selected to run the app in “Therapy Mode”,
where a therapist can control values such as thresholds to define gait health, or the RAS tempo.
65
During this screen, the user will be shown the two devices that the app connects to. Once all
selections and connections are made, the user can press the “Go” button to begin.
The main window of the app will have a “Start” button for the user to select once ready to walk.
This tells both Arduinos to begin data processing through a Bluetooth transmission. Instantly, one
graph for each leg will become populated with real-time processed data from each Arduino. When
aligned and misaligned peaks occur, the user will hear each respective sound. The drum machine
will begin once the initialization process is complete, and will continue indefinitely.
5.4 Mobile App Implementation
This tool was developed based on a combination of rhythmic and mutual entrainment, thus similar
to previous interactive RAS tools [17, 136], this tool establishes mutual entrainment between the
user and the app and provides a modulated auditory cue back to the user.
First, entrainment is established by playing a fixed tempo that is predetermined by the user’s
walking abilities and therapy goals. To initiate the app, the user must walk consistently to establish
a baseline, and stride rate is calculated based on collected MMG peaks. A simple auditory cue is
presented at the pre-set tempo once 10 steps are timed to be within 2 standard deviations of each
other. Studies show that if the tempo provided by RAS is too different from spontaneous walking
tempo, the RAS presented will conflict with natural rhythm and have a negative impact on gait
dynamics [17].
Second, interactive RAS feedback is provided using temporal features selected from live MMG
measurements. In this tool, user gait patterns will be compared against typical gait patterns and
determined to be typical or atypical [55]. If gait patterns align, users will be presented with a
musical cue that sounds pleasant, whereas if patterns do not align, an unpleasant sound will be
presented. The tool will also track how many typical steps are taken, and if a given threshold is
reached, a layer of music from a programmed drum kit will be added to the auditory feedback.
Positive patterns will increase layers of the drum kit as the user takes successive steps, whereas
unsuccessful steps will need to be tallied up to remove drum kit layers. Studies have shown the
complexity of movement and locomotion [14, 16], thus we propose a dual system of fixed RAS
66
will stimulate pattern synchronization and then MMG-based biofeedback for stimulating temporal
cues of movements to improve gait dynamics and neural reorganization in the long-term.
In terms of therapy protocol, users will perform 30 minutes of GaitTool App-assisted walking
daily for 8 weeks. In a recent study looking at motor recovery during early gait rehabilitation in
neurological disorders, motor deficits were seen to improve in as early as four weeks with daily
gait training [138]. This at-home tool allows us to increase length of gait training thereby
enhancing neural reorganization and lasting motor improvements [139]. Additionally, this study
will allow us to gain knowledge on optimizing training parameters for gait therapy.
5.5 App Evaluation
In this section, we discuss initial results from the evaluation of the GaitTool App prototype. After
device validation, and therapy validation, the GaitTool App will be ready for the market.
We will implement the application for Android operating system and tested it on a tablet (Samsung
Note 6 with Android 7.0 Nougat). Validation and prototype testing will occur in two stages. First,
laboratory testing will be done to evaluate the App for use with the clinical population. We are
currently recruiting participants for this study. Self-paced gait will be recorded offline and MMG
patterns will be analyzed for participants with diplegic cerebral palsy. Preliminary online testing
with feedback will then be performed in the laboratory to validate gait therapy performance.
Secondly, we will conduct a longitudinal study measuring the effects of an 8-week gait
intervention with the GaitTool App. MMG gait patterns and App use will be tracked to explore
the optimization of training parameters and in compliance of use of this tool.
5.6 Discussion and Conclusions
To our knowledge, this paper presents the first mobile application that plays auditory feedback
based on MMG muscle activity. Wearable MMG allow us to measure, analyze, and present real-
time auditory feedback in a way that is not only affordable and convenient, but also enjoyable.
67
This tool also allows us to track gait dynamics longitudinally, which is important for tracking
therapy outcomes and neural reorganization.
5.7 Acknowledgments
Thanks to Ka Lun Tam and Pierre Duez for their help with hardware and software design and
implementation, and the members of the PRISM lab for their ongoing support. Special thanks to
Dr. Thaut for his expertise in movement sonification and assistance in identifying suitable auditory
feedback.
68
Chapter 6
Conclusions
6.1 Summary of Contributions
Major contributions of the thesis are as follows:
1. Demonstrated the intermodal agreement between accelerometer-based MMG and surface
EMG muscle activity during pediatric gait using a PSO algorithm. Specifically, using a
combination of amplitude threshold ( ), moving window size ( ), and minimum percent
of EMG-MMG activity overlap (𝛿) between modalities, we discovered temporal alignment
(balanced accuracy in excess of 75%) of electrical and mechanical signals at the tibialis
anterior, vastus lateralis, and biceps femoris, and temporal misalignment (~50% balanced
accuracy) at the lateral gastrocnemius. These findings suggest that the relationship between
electrical and mechanical muscle activities can be more complicated in dynamic quasi-
periodic motor tasks than in simple, isolated contractions.
2. Demonstrated that spatiotemporal patterns of MMG during the gait cycle differ from those
of EMG. In contrast to isometric and isokinetic contractions, during a complex dynamic
motor task, the displacement of the muscle fascicles and the corresponding MMG signal
do not necessarily follow EMG activity. The timing and power distribution differences
between these modalities may in part be related to muscle fascicle length changes that are
unique to muscle motion during gait.
3. Demonstrated the mechanical synergies associated with lower limb MMG activity during
walking and running gait. Understanding mechanical synergies during motor tasks is useful
in detecting pathology, providing feedback about movement patterns, and in controlling
assistive technologies and robots.
4. Developed an Android-based application that presents auditory feedback based on the
alignment of MMG activity between the tibialis anterior and lateral gastrocnemius. This
application records and analyzes MMG in real-time, sets a rhythmic stimulus based on the
69
user’s cadence, and controls a drum kit based on typical and atypical MMG patterns. This
is the first app to combine fixed rhythmic stimulation with sonification to gait therapy
targeted for at-home use.
6.2 Future Work
To further develop the use of MMG in dynamic motor tasks, such as gait, the following may be of
interest to future studies.
The proposed MMG segmentation method (Chapter 3) provides a framework for comparing
differing modalities based on pre-defined criteria, which can be tailored to specific target
populations or movement types. Future studies might focus on exploring MMG features in client
populations, as well as exploring different features in dynamic MMG, such as frequency features,
which can be helpful in detecting different contraction types [59]. In particular, wavelet-based
MMG analysis [59] for automatic detection of concentric and eccentric contractions during gait
merits further exploration.
The segmentation method also requires a small subset of training data, which may change for
different populations or movement types. Thus, training of the algorithm should be targeted to
each movement type. Additionally, future work should focus on creating a generalized detection
algorithm that is muscle-specific instead of participant-specific. This algorithm can be
implemented as a bimodal EMG-MMG tool for: the detection of voluntary muscle activity amid
motion artifact; the identification of pathologies in movement patterns; and, the discrimination
between various types of movements, such as walking and cycling. Literature suggests that such
multi-modal (hybrid EMG-MMG) systems can enhance activity detection beyond that achievable
with a single modality (EMG or MMG) [52, 72, 132].
The reported synergy analysis can be extended to combine EMG and MMG as complementary
modalities in a neuromechanical model since together, these signals provide an understanding of
the neural input and mechanical output of muscle contraction, coordination, and movement. A
neuromechanical model might inform the measurement of rehabilitation outcomes, and provide a
low-cost method for tracking movement, and sports performance. Future work should also
70
investigate mechanical synergies in populations with gait disturbances, and extend synergy
analyses to other movements, such as reaching tasks.
In terms of the GaitTool App, future work should focus on implementing wireless MMG sensors,
testing the app with the target population, and eventually expand clinical testing to include other
neuromuscular populations. The app can be applied to different movement tasks, such as jumping,
or upper limb movements, such as reaching. Additionally, specific training programs can be built-
in to include therapy goals and therapist notes. From a therapist or clinician perspective, tools can
be added to monitor muscle activity, through mechanical synergies, for example, and track therapy
progression and neuroplasticity.
6.3 Publications
6.3.1 Journal Articles
Plewa, Katherine, Ali Samadani, and Tom Chau. "Comparing electro-and mechano-myographic
muscle activation patterns in self-paced pediatric gait." Journal of Electromyography and
Kinesiology 36 (2017): 73-80.
Plewa, Katherine, Silvia Orlandi, Ali Samadani, and Tom Chau. "A Novel Approach to
Automatically Identify Coincident Activity Between EMG and MMG Signals." Journal of
Electromyography and Kinesiology Submitted Nov 2017.
Plewa, Katherine, Silvia Orlandi, Kei Masani and Tom Chau. "Muscle Synergy Patterns of
Mechanical Activity during the Gait Cycle using MMG and EMG." Frontiers in Human
Neuroscience Submitted February 2018.
6.3.2 Conference Presentations
K. Plewa, M. Silverman, S. Orlandi, T. Chau, M. Thaut (2017). Designing a Wearable MMG-
based Mobile App for Gait Rehab. IEEE Life Sciences Conference: Sydney, Australia. December
2017.
K. Plewa, O. Paserin, T. Chau (2015). Feature Extraction in Accelerometer-Based
71
Mechanomyography During Pediatric Gait. IEEE Engineering in Medicine and Biology
Conference: Milan, Italy. August 2015.
Plewa, K. T. Chau (2015). Dynamic Noise Reduction and Feature Extraction in Accelerometer-
based Mechanomyography during Pediatric Gait. IUPESM World Congress on Medical Physics
and Biomedical Engineering; Toronto, ON. June 7-12, 2015.
72
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*small fist pump*