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Development of a novel link-segment model
for estimating lower back loading in paramedics
by
Peter Alexander Wetherall Galbraith
A thesis submitted to the School of Kinesiology & Health Studies in
conformity with the requirements for the degree of Master of Science
Queens University
Kingston, Ontario, Canada
September 2011
CopyrightPeter Alexander Wetherall Galbraith, 2011
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Abstract
Work conducted as part of this thesis evaluated the lifting techniques
of paramedics using a novel link-segment model that was validated
against a commercially available software package, 3D Static Strength
Prediction Program (3DSSPP).
Site visits to four paramedic services across the province were
conducted to gain information about bags weights and lifting
techniques. Twenty-five paramedics then visited the Biomechanics Lab
at Queens University to participate in testing sessions mimicking the
daily lifting and carrying tasks performed by paramedics on the job.
Participants were outfitted with the Xsens Motion Tracking System and
asked to lift and carry bags ranging from 5-20kg. Output from the
Xsens system was used in a 3D-inverse dynamic model to estimate
loading at the L5/S1 joint. The compressive and shear force estimates
at this joint are of particular interest given their correlation with low
back pain and injury.
Across all conditions the greatest compressive forces were seen during
bag pickup and bag release. Additionally, reaching forward 50 cm at
pickup increased peak spinal compressive loads by nearly 300N and
500N for a 5kg and 10kg handbag respectively. Not surprisingly, at bag
release greater trunk lean values were correlated with higher
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compressive force estimates. Single-shoulder backpack carries showed
similar loading characteristics when compared to double-shoulder
backpack carries. Shear force estimates remained well below
acceptable levels across all conditions.
Based on paramedic feedback, a supplementary testing session was
performed with a single participant to evaluate multi-bag carries and
stair climbing. The results of this testing session showed that loading
was reduced at pickup and release when the load was distributed
across two bags.
This research led to the development of four recommendations that
have been presented to the Association of Municipal Emergency
Medical Services of Ontario.
1. Paramedics should not lift single bags or a combination of bags
that exceed 20kg.
2. Prior to lifting, bags should be located as close to the
paramedic as possible.
3. When placing bags on the ground and when picking bags up
off of the ground, paramedics should use a squat lift technique
to prevent forward and side bending.
4. When multiple bags are carried the load should be evenly
distributed within bags and across sides of the body.
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Acknowledgements
I would like to start by thanking my supervisor Dr. Pat Costigan for his
help and guidance from start to finish. Not to mention saying that an
independent study on longboarding was entirely reasonable.
Dr. Joan Stevenson, without whom this project could not have
happened.
Thank you to my family (Mom, Dad, and Jamie, as well as my extended
family in Kingston and Calgary) for encouraging a healthy curiosity in
all things and supporting me throughout my life.
Rachel for being there at the end of some long days and always being
willing to discuss my research.
The many friends I have made in the department for always being
ready for a celebratory or conciliatory pint, and providing me with so
many lessons and experiences that could never take place in a
classroom.
Table of Contents
TOC \o "1-3" Abstract
Table 1 - Conditions presented to paramedics during in-lab testing. Allconditions included a 4m carry.
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Figure 1 - XSens Motion Tracking System sensors. Sensors are placedon the lower arms (1&2), upper arms (3&4), scapulae (5&6), upperback (7) and lower back (8).
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Chapter 1: Introduction
In August 2010, the Association of Municipal and Emergency Medical
Services (AMEMSO) contacted the Queens University Biomechanics
Lab in the School of Kinesiology and Health Studies to investigate the
weight and design of equipment bags carried by paramedics in the
province of Ontario.
The 72 certified land ambulance services in Ontario respond to an
estimated 1.5 million calls annually. In responding to these calls,
paramedics carry their equipment bags over long distances, up and
down stairs, and through confined spaces. Given that paramedics can
respond to a number of calls per shift, it is not surprising that the
various types, size, and weights of these bags are a concern. Despite
the fact that common equipment is carried in the bags, there are no
standards governing the size and number of bags or the weight carried
in any particular bag.
The results of this research provides insight into the forces
experienced by paramedics while lifting equipment bags on the job
and details the aspects of lifting that increase the risk of injury as put
forward by the National Institute for Occupational Health and Safety
guidelines (Waters et al. 1993). From these findings, the researchers
recommend ways to improve the lifting conditions and reduce the
strain on paramedics while lifting and carrying bags on the job.
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For paramedics, two common scenarios occur: (1) when they are
required to lift an individual from the ground while holding a bag in one
hand, and (2) when they have to guide themselves and the patient
around or over obstructions. The variety of sites that paramedics visit
means that paramedics often experience awkward postures. Studies
assessing paramedic injuries have shown that low back strains are a
major source of time off work and may be one reason why paramedics
have such high injury rates (Hogya & Ellis 1990; Okada et al. 2005)
Clearly, it is important to understand the loads experienced by
paramedics across a variety of lifting conditions. An understanding of
the magnitude of these loads while performing paramedic work is
essential to determine if the weights and lifting techniques are safe.
Biomechanical measures that have been correlated with increased risk
of low back pain include: peak compressive force, peak shear force and
the cumulative load experienced by the L4/L5 or L5/S1 joint, (Norman
et al. 1998; van Dien & Toussaint 1997). Compressive forces act
along the craniocaudal axis of the spine and under normal loads the
vertebral body withstands most compressive forces. However, the
extreme case can lead to disc herniation or prolapse (Roaf 1960).
NIOSH guidelines (NIOSH, 1981) have put forward a maximum
acceptable limit of 3400N and maximum permissible limit of 6400N of
compressive force. The acceptable limit represents a compressive
force value that should be safe for 75% of women and 99% of men.
Beyond 6400N higher risk for injury is predicted. Shear forces act in
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the medial-lateral and anterioposterior directions along the spine and
are another risk factor for low back pain (Kerr et al. 2001). McGill et. al.
(1998) have put forward a maximum acceptable limit of 500N and a
maximum permissible limit of 100N for shear force along the
anterioposterior axis of the spine. Traumatic events are not always the
cause of low back pain. Repetitive loading of even small amounts can
lead to low back pain and injury over time. If we consider each of these
loading instances a single trauma, the cumulative loading is the
accumulation of small traumas over time. As no cumulative exposure
limits have been found in the literature, determination of safe and
unsafe tasks based on cumulative loading is difficult.
Link-segment modeling is often used to quantify these compressive,
shear and cumulative loads of the task at hand. Link segment models
(LSM) use basic physics equations and represent the body as a series
of rigid, connected segments. LSM incorporate anthropometrics and
individual measurements of motion as well as estimates of the external
forces to provide an estimate of the forces and moments imposed on
the joints. Most LSM of the spine also estimate the surrounding
musculatures force contribution that is often substantial and,
therefore, must be considered. The compressive force contributed by
the back extensors, which includes many different muscles, is often
estimated by representing all muscles as a single muscle equivalent.
Joint load estimates increase when dynamic parameters of motion are
included and when the lifting task is asymmetrical (Marras & Granata
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1995). For better estimates of joint load, it is important to use a fully
dynamic three-dimensional model.
We hypothesized that heavier loads would lead to higher compressive
force estimates and that lifts using backpacks and shoulder bags would
produce lower estimates than those conditions using handbags
because of the improved load location. Additionally, it was expected
that those paramedics that adopted a squat posture when releasing
bags would experience lower compressive forces than those who chose
to lean forward to release the bag. It was hoped that this study would
enhance the bag selection and design criteria as well as producing
guidelines for appropriate lifting technique for paramedics.
At the conclusion of this research, the results were presented to
AMEMSO in the hopes of improving bag weighting and lifting policies
across the province.
Chapter 2: Review of Literature
2.1 Review of Modelling Literature
Human link-segment models have been used in video games,
rehabilitation, and biomechanical settings to record and gain
understanding about human motion. The human body is modeled as a
series of connected rigid links based on subject-specific
anthropometric parameters. Link-segment models are then used as
input to inverse models using basic force and moment equations to
quantify the forces and moments experienced by the spine, especially
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when examining lifting (Kingma et al. 2001; Abdoli-Eramaki et al. 2009;
Norman et al. 1998; Potvin, McGill, & Norman 1991). To better
estimate loading on the lower back the muscular contributions must be
taken into account. To include the inertial components required in a
dynamic inverse model, effective motion capture tools are required to
measure the motion under investigation.
2.1.1Capturing Human Motion
Before any investigation can be made into the kinetic or kinematic
properties of human motion, researchers must be confident that the
recorded motions closely represent the actual motions that took place
during data collection. Early motion capture techniques relied heavily
on film recordings and are time consuming to process, often requiring
manual digitization and error checking throughout the entire process.
The advent of digital video recordings has sped up many of these
processes and is still a key component of modern biomechanical tools
such as HU-M-AN (HMA Technology, Canada), 3DSSPP, (University
of Michigan, USA) and 3DMatch (University of Waterloo, Canada).
Other motion capture systems require participants to be instrumented
with light emitting diodes or reflective surfaces. These motion capture
systems, such as Vicon Nexus (Vicon Motion Systems, USA) and
Optotrak Certus (Northern Digital Inc, Canada), require line of sight
and multiple cameras to automatically record 3-dimensional
movement, and boast sub-millimeter accuracy. These systems can
have large capture volumes but are not particularly portable. Local
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coordinate systems are created using the sensors attached to each
segment. The known orientation between relevant anatomic landmarks
and local coordinate system allows the creation of an anatomical
coordinate system for each segment. Link-segment models are then
built from the anatomical coordinate systems and known
anthropometrics. As each segment is located in a global reference
frame regardless of the orientation and position of other segments,
measurement errors remain relatively constant across all segments.
A relatively new technology has emerged in the past decade allowing
researchers to capture motion in the field without the need for line of
sight or large systems. Such systems rely on accelerometers,
gyroposcopes, and magnetometers and advanced software to produce
reliable estimates of body segment position and orientation
(Roetenberg, Luinge, & Slycke 2009). The Xsens Motion Tracking
System (Xsens, The Netherlands) is one of these systems and has been
used in motion capture labs and for video game and movie motion
capture. These systems are highly valuable for the commercial setting
given their real-time capabilities and ease of use. However, the
scientific community has questioned the accuracy of these systems for
scientific research (Cutti et al. 2006; Luinge & Veltink 2005; Damgrave
& Lutters 2009; Brodie, Walmsley, & Page 2008). The main problem is
that the measurements drift due to the reliance on magnetometers to
determine the sensors heading. Aligning each sensors local
coordinate system with the supposed underlying anatomical
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coordinate system approximates anatomical coordinate systems. Link-
segment models developed using inertial sensors must be built
sequentially from proximal to distal (or vice-versa). In most cases, the
origin of the most proximal segment is assumed to exist at the origin
of the global coordinate system. The most proximal segment pivots
about the origin based on the orientation of the attached sensor. The
most proximal segments endpoint is used as the next segments start
point and the process is repeated. Thus errors are accumulated as the
LSM is built from proximal to distal, and the greatest position errors are
seen in the distal segment. Image-based motion capture systems such
as Vicon Nexus are able to avoid cumulative errors in segment
orientation because they locate each segment in a global reference
frame.
The effect of these errors on kinetic and kinematic parameters has
been evaluated. Godwin (2009) compared a link-segment model built
using Xsens sensors to one using Vicon Nexus and found distal
segment endpoint RMS errors of 136 mm, 138 mm, and 101 mm in the
anterior-posterior(AP), medial-lateral(ML), and inferior-superior(IS) axes
respectively. These errors led to flexion moment RMS errors of 12 Nm,
10Nm, and 4Nm along the AP, ML, and IS axes respectively. These
values may seem small but represented between 10% and 30% of
peak moments across a variety of trials.
Since Godwins work, improvements have been made to the Xsens
system by improving the filtering of the accelerometers and
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gyroscopes. In 2005, Zhou, Hu & Tao showed average error for distal
endpoint locations ranging from 10-70mm, and in 2010, Zhou and Hu
showed that the system now has RMS position errors of 9mm, and
drifts of less than 5 mm/s when used while performing daily
activities.These improvements appear to be made by improvements to
the proprietary Kalman filter; a technique that combines the predicted
and observed values for the accelerometers, gyroscopes and
magnetometers. For an explanation of this process see Brodie,
Walmsley & Page (2008).
These improvements in Xsens output have led to improvements in
kinematic accuracy. Cutti et al. (2010) reported RMS joint angle errors
of 1.4 and 1.8 for the hip and knee angles when compared with a
standard goniometer and errors of approximately 2 at the hip and
knee. Basic drawing tests* have shown the Xsens system to be
accurate within 0.5 cm using kinematic modeling over periods of 25
seconds and has been deemed acceptable in a neurorehabilitiation
setting (Bai et al. 2011). When investigating gait parameters similar
repeatability measure values were found when comparing the Xsens
system (Cloete & Scheffer 2010) to Vicon Nexus (Kadaba et al 1989)
and Polhemus Liberty (Mills et al 2007).
*These drawing tests required participants to repeatedly trace a triangle. Thus when the link-segment
model tightly matches the dimensions of the triangle over a series of repetitions we assume that the
model is valid.
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2.1.2Modelling the Shoulder Joint
Godwin (2009) notes one limitation of her model was that the
glenohumeral joint is unable to translate relative to the spine.
However, there can be as much as 150 mm of protraction (anterior
translation) of the glenohumeral joint itself (Albert et al. 1998),
suggesting that a rigid connection is invalid. As noted by Godwin, the
inability to represent shoulder protraction may account for some of the
errors in distal segment endpoint location.
Godwin (2009) and others simply assume that the shoulder joint
maintains a constant orientation and position relative to the upper
body segment (Cutti et al 2008; Rau, Disselhorst-Klug, & Schmidt
2000; Rab, Petuskey, & Bagley 2002). In these cases the glenohumeral
joint is assumed to act as a hinge joint with no shoulder translation.
Godwins model is an example of an open loop system where each
segment is linked to only one other segment in a chain from back
segment to hand segment. Other models (van der Helm 1994a,
Dickerson, Chaffin, & Hughes 2007; Maurel et. al. 2010) have
constrained shoulder joint translation using a closed link between the
scapula, clavicle, and upper back. In this way a triangle is formed
between the clavicle, scapula and back and thus shoulder joint
translation is limited but not rigid. Yang et al (2010) have suggested
that closed-loop models have higher accuracy and fidelity than open-
loop models.
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Three bones make up the shoulder complex: the clavicle, humerus and
scapula. The only linkage between the axial skeleton and the upper
extremity occurs where the clavicle attaches to the sternum to form
the sternoclavicular joint. Sternoclavicular range of motion has been
estimated at 20 degrees of protraction (elevation), 60 degrees of
forward flexion, and 10 degrees of axial rotation (Inman, Saunders &
Abbott 1944; van der Helm 1994b; van der Helm & Pronk 1995). The
glenoral fossa of the scapula and the head of the humerus form the
glenohumeral joint that permits movement of the upper arm. To
represent motions such as shoulder shrugs, and pinching the shoulder
blades, LSMs built using inertial systems must instrument the clavicle
or scapula to gauge shoulder joint translation.
Recommendations exist about how best to model the shoulder
complex (Wu et al. 2006); however, problems arise when trying to
securely and comfortably attach a sensor over the clavicle (Cutti et al
2008). Additionally, the shoulder joint centre is difficult to determine
because it is located deep below the skins surface hiding the bony
landmarks that are necessary for joint centre determination (Rau,
Disselhorst-Klug, & Schmidt 2000).
The effect of shoulder joint translations on inverse dynamic model
output should be considered to see if differences between shoulder
models lead to substantial differences in force estimates. The only
research we found that attempted to quantify this effect showed that
shoulder translation had a significant effect on positions and
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accelerations of arm segments, but did not have a statistically
significant effect on L5/S1 moment prediction (Albert et al. 1998). Rab
et al. (2000) argued that shoulder joint centre determination errors of
20mm had a negligible effect on shoulder kinematics. It may be the
case that shoulder joint centre differences have a small effect on
kinetic and kinematic results.
2.1.3Kinetic Parameters
Once link segment models have been built, researchers can
investigate the kinetic or kinematic parameters of the model.
Kinematic parameters such as: joint angle, segment angle, range of
motion, displacement and velocity can be used to investigate
differences between groups of individuals or attempt to describe
specific aspects of human motion. Alternatively, kinetic parameters
can be investigated to gain an understanding of the forces and
moment to which the body is subjected. Newtonian physics, built upon
the fundamental equations of motion of a rigid body (Zatsiorsky 2002)
are used to determine the mechanical forces on various body
segments. These parameters are often broken down into their
component vectors and expressed in the local coordinate system of
the segment of interest. For example force vectors at the L5/S1 disc
are often broken down into: a compressive force acting cranio-
caudally, and two shear forces acting anterior-posteriorly and medial-
laterally.
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2.1.4 Estimating Muscular Contribution
Calculations based on equations of motion do not provide a true sense
of the loading at specific joints. To produce better estimates of joint
loading, many models include the force contribution made by the
surrounding musculature. As many as 104 different components have
been incorporated into spinal models to balance the forward flexion
moment using passive and active tissues (Callaghan & McGill 2001),
while others use a single back extensor model (Norman et al. 1998).
Mathematically -driven optimization models (Brown & Potvin 2005)
have also been used to estimate spinal loading.
Complex electromyography assisted models (Gagnon et al. 2011;
Mientjes et al. 1999; Davis, Marras, & Waters 1998) include the
activation of dozens of muscles to estimate loading on the lower back.
Potvin et al. (1991) recorded EMG activation of 11 different muscles
during symmetrical squat and stoop lifts. They partitioned the reaction
moment into 11 muscles and 7 ligaments based on each muscles
activation and each ligaments strain based on lumbar flexion angle.
Partitioning the reaction moment depended upon assumptions of:
ligament stress-strain curves, muscle lines of actions, muscle cross-
sectional area, and the modeled relationship between muscle
contraction velocity, activation and produce muscular force. A large
number of assumptions must be made to produce EMG-assisted
models.
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Given the number of calculations and assumptions required to build
EMG-assisted models some researchers have elected to avoid EMG
altogether. Optimization models are mathematically-driven functions
that partition the reaction moment based on assumed muscular
activation patterns. In essence, these models assume the central
nervous system is acting to achieve biomechanical equilibrium by
minimizing some objective function such as muscular contraction (An
et al. 1984, Crowninshield & Brand 1981; Nussbaum, Chaffin, &
Rechtien 1995), joint force (Brown & Potvin 2005), metabolic energy
consumption (Davy & Audu 1987), or some combination of these
factors (Bean, Chaffin, & Schultz 1998; Pel et al. 2008; Seireg & Arvikar
1973). Again, a large number of assumptions are required. One
problem with optimization models is their inability to accurately predict
muscular co-contraction, which may lead to underestimates of joint
loading (Cholewicki, McGill, & Norman 1995).
An alternative to optimization models, while still avoiding EMG, is a
single equivalent muscle extensor model, in which all muscles that
assist in flexion are assumed to act as a single muscle group. This
model assumes a single extensor muscle group is the sole means by
which the body counterbalances the forward flexion moment, and acts
exclusively about the flexion axis. It should be noted that because this
muscle group is modeled as acting along the long axis of the spine it
cannot counterbalance moments about the inferior-superior (IS) or
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anterior-posterior (AP) axes of the spine. However, it does produce a
force vector that increases compressive estimates.
Decisions regarding moment arm length are important in this case as
small length differences can lead to large variations in final joint
loading estimates. McGill & Norman (1987) state that 5 cm is a
commonly used value for the single extensor muscle moment arm,
while Norman et al. (1998) have used a moment arm length of 6cm.
These values are in keeping with the real moment arm length for the
erector spinae muscle group range of 4.9-6.4 cm (Jorgensen et al.
2001). To factor in some muscular effort to balance IS and AP shear
forces, varying the line of action within realistic ranges has been
suggested (van Dien & de Looze 1999). These variations can change
compressive force estimates by more than 100N and shear force
estimates by more than 50N (Nussbaum, Chaffin, & Rechtien 1995).
Varied lines of action may not substantially change model output given
that heavy lifts commonly exceed the action limit of 3400N of
compressive force (NIOSH 1981). These variations may be more
relevant for shear force output given that the maximum acceptable
limit for shear force is 500N (McGill 1998).
It is important to understand how different muscle models influence
force output estimates. Potvin et al. (1991) showed that for a 32-kg lift
at peak lumbar spine flexion the erector spinae contributed 74%
(stoop) and 83% (squat) of all compressive force contributions made
by muscular or ligamentous tissues. This indicates that in sagittal
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plane lifting the erector spinae are the primary contributors to
muscular compressive force, and a single extensor muscle model may
be an appropriate method in this instance. Cholewicki, McGill, &
Norman (1995) found that optimization models predicted
approximately 30% lower L4/L5 compressive force values than EMG-
assisted models, possibly because the optimization model cannot
accurately predict co-contraction of antagonistic muscle pairs.
There are inherent problems to each model when estimating muscular
force contributions. Single muscle extensor models are simplistic and
do not attempt to represent the true nature of the back musculature
while specifically lacking the muscles acting at oblique angles to the
spine that stabilize the spine during twisting and asymmetrical
motions. EMG-assisted models rely on a length-strength and force-
activation relationship for each muscle investigated and these
relationships may be flawed, especially when considering different
strength capabilities across individuals. Additionally, while stability is
visibly maintained during most lifts performed during data collections,
Brown & Potvin (2005) noted that EMG-assisted models may produce
situations where equilibrium is not maintained. As noted before,
optimization models rely on mathematical solutions to determine the
force contributions of the musculature in the back, and often do not
include agonist-antagonist muscular co-activation. In each case some
drawbacks are accepted with the goal of achieving more realistic
loading estimates. Despite (and possibly because of) their simplicity,
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single equivalent extensor models continue to be used in
biomechanical research.
2.2 Review of Lifting Literature
Lifting is a risk factor for low back pain (Marras et al. 1995; Chaffin &
Park 1973). The 2009 U.S. Department of Labors Annual Survey of
Occupational Injuries found that 46% of musculo-skeletal disorders
were associated with the back and required on average 7 days off
work. This survey also found 116,530 injuries requiring time off work
were the result of overexertion while lifting. Studies have examined
psychosocial, physiological and biomechanical criteria to determine
risk factors for low back pain. These factors include: previous instances
of low back pain, job satisfaction, job stress, repetitive lifting, heavy
lifting, forward flexion, axial twisting, overhead reaching and hand
coupling to name a few (Hoogendoorn et al. 2000; Kerr et al. 2001;
Marras et al. 1995, 1999; Woolf & Pfleger 2003; Frymoyer et al. 1983;
Brinckmann et al. 1998). Jobs requiring frequent and heavy lifting have
been associated with increased risk of disc herniation and low back
pain in general (Kelsey et al., 1984, Kelsey & White, 1980). Attempts to
limit low back injuries have focused on improving postures while lifting
and reducing loads (NIOSH 1981; Waters et al 1993).
2.2.1Compressive Force Guidelines
Compressive forces act along the IS axis of the spine and under normal
conditions the vertebral body withstands most compressive forces.
However, the extreme case can lead to disc herniation or prolapse
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(Roaf 1960). Cadaveric research has shown that ultimate compressive
forces may been in the range of 2100- 9600N (Brinckmann et al.
1988), having a mean of 4400N with a standard deviation of 1880N
(Jger & Luttman 1989).
In 1993, the National Institute for Occupational Safety and Health
released their lifting equation that identified hazardous lifting tasks
based on biomechanical, psychosocial, and physiological factors. Two
compressive limits are put forward: the action limit (AL) of 3400N, and
maximum permissible limit (MPL) of 6400N. The biomechanical
criterion for the equation is based on research showing that: spinal
compressive forces of greater than 3400N may increase the risk for
low-back injury and injuries may become quite likely beyond 6400N.
Waters et al (1993) argue that if the data were normally distributed
21%-30% of lumbar segments would fail when loaded with a force of
3400N given the ultimate compressive force values put forward by
Brinckmann et al (1988) and Jger & Luttman (1989). Interestingly,
Norman et al (1998) found that the mean peak compressive force of
individuals reporting low back pain was 3423N. The AL of 3400N is a
conservative estimate for a healthy working population since cadaver
lumbar segments may have lower tolerance limits because of declines
in lumbar strength with age, as well as changes in bone mineral
content (Hansson & Roos 1981). However, when ensuring workplace
safety, conservative limits should be used.
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2.2.2 Shear Force Guidelines
Shear forces act in the medial-lateral and anterioposterior directions
along the spine and are a risk factor for low back pain (Kerr et al.
2001). Krypton et al. (1995) found shear force tolerance limits in
cadavers of between 1700N and 2900N. While less work has been
done in this area, some guidelines have been developed based on
thinking similar to that of the NIOSH Equation. McGill et al. (1998) has
put forward an action limit of 500N and a maximum permissible limit of
1000N; these limits are akin to the 3400N action limit and 6400N
permissible limit for compressive force. A shear force limit of 500N has
been used with reasonable success to predict which workers reported
low back pain (Daynard et al. 2001). Tasks that keep compressive
forces below 3400N and shear forces below 500N are unlikely to
increase the risk of injuries.
2.2.3Model Complexity
Another important point to consider in developing a link segment
model is whether to include dynamic components in force and moment
calculations. Static models are easier to implement, as fewer
calculations are required. Static and quasi-static models have been
developed and applied successfully for many years particularly for
slower motions, but in cases where inertial contributions are non-
negligible, dynamic models may be more accurate in predicting
loading due to the inertial effects of the load and body segments
(Marras & Granata 1995; McGill & Norman 1985; Lindbeck & Arborelius
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1991; de Looze et al. 1994). Substantially higher predicted loads have
been seen when acceleration components are included in force
calculations when compared to what is otherwise the same task (Jger
& Luttman 1989).
Asymmetric lifting is a common occurrence for most tasks due to
differences between load origin and destination, movement
requirements, obstructions or a variety of other reasons. To that end,
postural symmetry is unlikely to happen in working environments. In
spite of this many models only take 2D motions into account when
analyzing lifting (Albert et al. 1998; Anderson et al. 1985; Waters &
Garg 2010). Underestimations of the peak torque have been shown as
high as 60% when loads are placed at 90 to the sagittal plane
(Kingma et al 1998). Lift asymmetry is identified in the NIOSH lifting
equation as a factor that reduces the maximum load that an individual
can safely carry (Waters et. al., 1993) It is suggested that researchers
may arrive at the wrong conclusions when a 2D model is used for tasks
with 30 or more of twisting (Kingma et al. 1998). For these reasons
three-dimensional dynamic models should be used when possible.
2.2.4Paramedic Injury Rates & Lifting Demands
Paramedics share similar lifting task demands with a variety of
professions such as nurses, nursing aides, and fire-fighters who are
required to support a patient and administer some level of care while
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simultaneously transporting the tools necessary to provide that care.
Studies of the nursing population have shown high levels of low-back
pain much of which is related to patient handling, (Jensen 1987,
Videman 1984) a task that paramedics are frequently required to
perform, with potentially greater strain due to awkward postures and
lower lift origins. The compressive force that the spine is subject to
while transferring a patient from one space to another has been shown
to exceed even the NIOSH maximum permissible limit of 6400N
(Marras et al. 1999). It is believed that workers cannot tolerate
compressive forces beyond this limit without increasing their risk for
injury (Waters et. al. 1993). Patient handling tasks expose paramedics
to potentially dangerous low back loads. These loads may be
contributing to the high back injury rates seen in paramedics (Crill &
Hostler 2005).
Back strain injuries (as classified by the International Classification of
Diseases, adapted, 8th revision (ICDA 1967)) are very common in the
paramedic population and account for 36% of all injuries; of those
more than half are caused by lifting activities (Hogya & Ellis 1990).
This same study found that each instance of reported back strain led
to, on average, 4 days of time off work. High paramedic injury rates
are not just a North American phenomenon. A survey of Japanese
paramedics and emergency medical technicians found that 25% of
respondents had experienced a low back problem in the previous 12
months, and 1/5th had experienced pain for 30 days or more in the
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same time span (Okada et al 2005). Patient handling may not be the
only contributor to injury. Spending as little as 10% of working time in
30 of trunk flexion has been shown to increase the risk for developing
low back pain (Hoogendoorn et al. 2000). Paramedics are often
required to bend over to lift and interact with the patient and when
administering care in the back of the ambulance may spend much of
their time in forward flexion. Due to the varied and demanding nature
of the job it seems inevitable that paramedics are exposed to those
factors that lead to low back pain. Work must be done to improve the
working environment for paramedics to prevent injuries and time off
work.
Chapter 3: Creating the Link-Segment Model
3.1 Introduction
The goal of this research was to understand the loads generated by
individual bag lifts performed by paramedics. Link-segment modeling
was used to meet this goal; in this case a novel three-dimensional
dynamic hands-down model was developed that incorporated the
output from a system of Xsens motion trackers.
3.2 Review
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Human link-segment models have been used in video games,
rehabilitation, and biomechanical settings to record and gain
understanding about human motion. Link-segment models are then
used as input to inverse models using basic physics equations to
quantify the forces and moments experienced by the spine, especially
when examining lifting (Kingma et al. 2001; Abdoli-Eramaki et al. 2009;
Norman et al. 1998; Potvin, McGill, & Norman 1991). To include the
inertial components required in a dynamic inverse model, effective
motion capture tools are required to measure the motion under
investigation. In order to better estimate loading on the lower back the
muscular contributions must be taken into account.
A relatively new technology has emerged allowing researchers to
capture motion in the field without the need for line of sight or bulky
systems. Systems, like the Xsens Motion Tracking System (Xsens, The
Netherlands), rely on accelerometers, gyroposcopes, and
magnetometers and advanced algorithms to produce reliable
estimates of body segment position and orientation (Roetenberg,
Luinge, & Slycke 2009). Body segment positions, and accelerations can
be used as input for inverse models to determine kinetic properties
such as the net force and moment. Link-segment models developed
using inertial sensors must be built sequentially from proximal to distal
(or vice-versa). Thus errors are accumulated as the LSM is built from
proximal to distal, and the greatest position errors are seen in the
distal segment. The effect of these errors on kinetic and kinematic
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parameters has been evaluated. Godwin (2009) found wrist position
RMS errors of 136 mm, 138 mm, and 101 mm in the anterior-
posterior(AP), medial-lateral(ML), and inferior-superior(IS) axes
respectively, leading to moment RMS errors of 12%, 17%, and 22%,
about the AP, ML, and IS axes respectively, as a percentage of peak
moment.
Recent improvements in Xsens output have led to improvements in
kinematic accuracy. Cutti et al. (2010) reported RMS joint angle errors
of 1.4 and 1.8 for the hip and knee angles when compared with a
standard goniometer and errors of approximately 2 at the hip and
knee. Basic drawing tests, requiring subjects to repeatedly trace the
same pattern and then checking for fidelity,have shown the Xsens
system to be accurate within 0.5 cm using link-segment modeling over
periods of 25 seconds and has been deemed acceptable in a
neurorehabilitiation setting (Bai et al. 2011). When investigating gait
parameters similar repeatability measure values were found when
comparing the Xsens system (Cloete & Scheffer 2010) to Vicon
Nexus (Kadaba et al 1989) and Polhemus Liberty (Mills et al 2007).
Godwin (2009) notes one limitation of their model was that the
glenohumeral joint was modeled as a rigid link between the distal end
of the humerus and the spine . However, there can be as much as
150 mm of glenohumeral joint protraction (Albert et al. 1998),
suggesting that a rigid connection is invalid . The inability of the
shoulder to protract may account for some of the errors put forth by
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Godwin. Thoracoclavicular range of motion has been estimated at 20
degrees of protraction (elevation), 60 degrees of forward flexion, and
10 degrees of axial rotation (Inman, Saunders & Abbott 1944; van der
Helm 1994b; van der Helm & Pronk 1995). Because the clavicle is
challenging to instrument, some models simply assume that the
shoulder joint maintains a constant orientation and position relative to
the upper body segment (Godwin 2009; Cutti et al 2008; Rau,
Disselhorst-Klug, & Schmidt 2000; Rab, Petuskey, & Bagley 2002).
Thus, the humerus rotates about the shoulder joint centre while the
shoulder joint centre does not move at all. Other models have sought
to monitor clavicular or scapular motion to track shoulder joint
translation (Dickerson, Chaffin, & Hughes 2007; Maurel et al 2010; van
der Helm 1994a). Yang et al (2010) have suggested that maintaining a
closed loop between scapula, clavicle, and back segments is important
to improve accuracy and fidelity in shoulder models.
The only research we discovered that attempted to quantify this effect
showed that shoulder translation had a significant effect on positions
and accelerations of arm segments, but did not have a statistically
significant effect on L5/S1 moment prediction (Albert et al. 1998).
Once link segment models have been built, researchers can
investigate kinetic or kinematic parameters of the model. Newtonian
physics, built upon the fundamental equations of motion of a rigid body
(Zatsiorsky 2002) are used to determine the mechanical forces on
various body segments. There have been reported differences in joint
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load estimates based on the inclusion of dynamic parameters and
asymmetrical lifting (Marras & Granata 1995). The muscular force
contribution must also be taken into account to produce accurate low
back loading estimates. EMG-assisted models, optimization models and
single muscle extensor models can be used to estimate this force
contribution. Kinetic parameters are often broken down into their
component vectors and expressed in the local coordinate system of
the segment of interest. Two commonly investigated parameters are
the compressive and shear forces at the L5/S1 joint. For these reasons,
it is important to use a fully dynamic three-dimensional model
including some measure of muscular force contributions to accurately
estimate joint loading for these lifting tasks.
3.3 Link Segment Model
3.3.1 Subject Instrumentation
Subjects were outfitted with Xsens Motion Trackers (MTx) sensors on
the thoracic spine and lumbar spine, as well as the left and right
scapulae, forearm and lower arm (Figure 1). By aligning the MTx
sensors with the long axis of the segments the orientation of these
body segments can be tracked during lifting tasks. The segment
orientations are used to create a representation of the subject under
investigation.
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7
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Figure 1 - XSens Motion Tracking System sensors. Sensors areplaced on the lower arms (1&2), upper arms (3&4), scapulae (5&6),upper back (7) and lower back (8).
Each sensors orientation was recorded using MT Manager (Xsens,
Netherlands) and sampled at 100Hz. All data was exported using MT
Manager and imported into Matlab (R2009a, The MathWorks) to
create the link segment model. All subsequent data processing was
also performed in Matlab, except where noted.
3.3.2 Building the Link Segment Model
The LSM is built sequentially from the pelvis to the hands and is
designed to accurately represent the body segments. The orientation
of each individual segment is determined by the orientation of the
attached sensor, except in the case of the head and neck segment,
clavicle segment and spine to sternum projection segment (which are
discussed later).
Anthropometric measurements of each individual are taken to create
segments representing: L5-L1,T12-T1, C7 to ear canal, clavicle, upper
arm, and lower arm. Back segments are measured by palpating the
spinous processes and counting up and down from C7 (the spinous
process that protrudes the most in forward flexion). The clavicle is
measured from the top of the sternum to the acromion process on the
clavicle by palpation. The upper arm is measured from the lateral edge
of the acromion process to the lateral epicondyle of the humerus. The
lower arm is measured from the estimated joint centre of the elbow to
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the second knuckle. This helps creates a LSM that represents each
individual subject participating in the study.
The pelvis, the fixed and unmoving base of the model, is the lowest
part and as this is a hands-down model the lower limbs are not
included. Attached to the top of the pelvis segment is the lower back
segment. Its motion is based on the orientation of the lumbar sensor
and its length, like all segments in the model, is based on subject
specific measurements. The upper back segment is connected to the
top of the lower back segment with its motion based on the output of
the upper back sensor. The head and neck segment is assumed to
maintain the same orientation as the upper back segment and is
projected up from the upper back endpoint. From the top of the upper
back segment, a rigid segment is projected forward towards the
sternum and represents the subjects trunk depth at this point. This
virtual segment helps link the axial skeleton and the humerus.
Next, two segments are created, a virtual clavicle segment that runs
from the sternum to the shoulder joint and a scapula segment, based
on the orientation of the sensor positioned above the scapula, that
runs from the top of the upper trunk segment to the shoulder.
If we assume that the clavicle, with a fixed length, can pivot freely
about the sternum, it will describe a sphere of possible clavicle
endpoints. The orientation of the scapula sensor determines the line
that pierces the clavicle endpoint sphere. The point of intersection
between the line and the sphere is taken as the shoulder joint.
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Figure 2 - How the orientation of the scapula sensor is used todetermine the position of the clavicular endpoint.
Models that have a rigid link between the spine and the shoulder, as
has been done, cannot represent shoulder joint translation, as would
be seen in reaching forward or reaching overhead. Our model more
validly represents these motions. As this model can incorporate
asymmetric motions, this procedure is repeated for the other shoulder.
On each side of the body, upper arm segments are attached to the
shoulder (clavical segment endpoints) and then lower arms (forearms)
are attached in turn. The hands are assumed to be rigidly attached to
the forearms. All required segment lengths are taken from anatomical
measurements of each individual subject.
3.3.3 Model Optimization
This model is optimized in a two-step process. The first step concerns
the length of the virtual segment connecting the spine and the
sternum (spine-sternum length), which is assumed to be rigid. During
an optimization trial, the subject holds a solid object (a piece of wood)
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and moves their arms around in a series of dynamic pushes, pulls,
twists and swings, while maintaining a grip on the object. Thus, the
distance between the lower arm endpoints (hands) is maintained
throughout the optimization trial. The distance between the hands is
calculated for each frame and subtracted from the true distance (the
real length of the object). The root mean squared (RMS) error is then
calculated. The spine-sternum length is increased and then this
calculation is repeated. The spine-sternum length was increased from
0 cm to the measured chest depth of the subject (usually about 15-
23cm) in 1 cm increments. At the end of this procedure we have
calculated RMS errors for each spine-sternum length for a single trial.
The calculated distance between hands is shown for 6 different spine-
sternum lengths in Figure 3. It should be noted that this process
improves accuracy when the predicted values are far from the real
values, and improves precision when the predicted values vary around
the real values.
Figure 3 - The effect of different spine to sternum projection lengthson the calculated 3D distance between lower arm endpoints during atrial where hand-to-hand distance was maintained. The dashedblack line represents the true hand-to-hand distance.
For each spine-sternum length, the RMS difference for the entire trial is
calculated and the trial with the lowest RMS value is selected as the
optimized spine-sternum length. This change is then incorporated into
the model as this provides the greatest reduction in segment endpoint
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prediction errors. During a series of optimization trials, this length was
found to be between 12 and 16 cm, and is in keeping with those values
found by measuring that same distance in MRI images from the Visual
Human Dataset (US National Library of Medecine, 2011). The RMS
difference for each spine-sternum length is presented in Figure 4.
Figure 4 - Finding the spine to sternum projection distance thatminimizes error in lower arm endpoint distance calculations.
A second optimization was implemented because of the potential for
misalignment between the Xsens sensors and the anatomical
coordinate system of the segment to which it is attached. Again, the
goal was to reduce the RMS error of the distance between the hands
using the same optimization trial used to estimate the spine-sternum
length. Using these trials the clavicle segments are rotated in 3
increments from -30 to +30about their original Z (mediolateral) axis
and the RMS error of the 3D distance between hands is calculated for
each 3 increment. The segment is then rotated by 1/3 of the angle
with the lowest RMS value. For example if the lowest RMS error value is
found to exist when the segment is rotated 9, then the segment is
rotated 3 in the experimental trials. The same optimization procedure
is repeated for the Y-axis (anterioposterior) and again the segment is
rotated by 1/3 of the angle with the lowest RMS error value. As the
result of rotating each clavicle is calculated separately, not reducing
the optimized rotation by 1/3rd would mean that each segment would
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be rotated to reduce all the error. This would overshoot the necessary
rotation and produce link-segment models that would be visibly
incorrect. These rotations are applied to the clavicle and then the
optimization process is repeated for the upper arms and lower arms in
sequence. Thus the reduction by 1/3rd also ensures a degree of
optimization sharing across segments. Pilot data showed that 6-9
degrees is usually selected for the clavicles about both axes, and 0-2
degrees for the lower arms.
Figure 5 demonstrates this improvement for a trial where the subject
maintained a constant distance of 65cm between the hands. The range
of measurement error is reduced from 17cm to 15cm and the mean
error is reduced from 69 cm to 65 cm, the actual length of the object
used in the optimization trials.
Figure 5 - Comparison of the raw and optimized projected distance
between lower arm endpoints. The actual length of the object is0.65m and represented by the thick black horizontal line
This optimization procedure is performed twice in two conditions:
hands 55 cm and 5 cm apart. The average of the four trials is used as
the optimization value.
As this optimization process, like the spine-sternum optimization
process, reduces RMS error relative to the real hand-to-hand distance,
the process improves accuracy when the predicted values are biased
in one direction, and improves precision when the predicted values
hover around the real value. The remaining noise in the system may
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be due to changes in hand orientation (which was not recorded),
sensor drift, and/or the sensors not moving when the segments were
(as a result of soft-tissue artifact, or the sensors shifting relative to the
clothing underneath).
3.3.4 Determining Loading
The researcher divided every lifting task into distinct phases based on
key loading instances. For hand carries these instances are: bag-
contact, full-bag-support, bag-leaving-hand, and bag-fully-on-ground;
for shoulder carries these instances are: bag-contact, full-bag-support,
hand-to-shoulder-transfer, shoulder-to-hand-transfer, bag-leaving-
hand, and bag-fully-on-ground. These instances define the beginning of
increases and decreases in support of the bag to enable a load timing
vector to be created for each hand and shoulder. These are called load
timing vectors because they change over the course of the trial but are
not vectors in the physical sense as they lack direction. These
instances are determined by visually inspecting every third frame of an
animation of the model in a method similar to watching a video
recording. Once important loading instances were observed the
animation was stopped and other relevant distance, position, or
velocity curves were inspected. Viewing only every third frame was
chosen to speed up the process of load timing determination; however,
when inspecting distances, positions and velocities all frames
surrounding the relevant instance were inspected.
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In all trials the participant (and consequently the animation of the
model representing the participant) initially reaches forward for the
bag. When the researcher determines that the subject has reached
furthest forward the animation is paused and the position curve of the
lower arm endpoint is inspected around this instance. The instance of
maximum reach is determined to be bag-contact. Playback of the
animation is resumed. Then the participant picks up the bag and pulls
it towards themselves. Again the animation is paused, and the
velocities of the relevant hand are inspected. The frame with the
highest peak velocity is chosen as full-bag-support. This decision was
based on the idea that participants would have full control of the bag
when their hand reached its peak velocity after pickup. Additionally,
Eger & Stevenson (2004) have shown that peak vertical hand forces
occurred between 0.07 and 0.18 seconds after the load has been
picked up. Peak vertical hand forces could only occur once the subject
is holding the box in their hands and has a high hand velocity.
The animation is resumed and in the case of shoulder carries, the
animation was again visually inspected up to the point where the hand
came close to the shoulder. At this point the 3D distance between the
lower arm endpoint (hand) and clavicular endpoint (shoulder) was
calculated and the instance of closest proximity was deemed to be the
point of hand-to-shoulder-transfer. This same process was repeated for
shoulder-to-hand-transfer (offloading). After the load was transferred
to the hand the animation was visually inspected for the instance when
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the participant had a substantial downward reach (putting the bag on
the ground). The hand velocities were then inspected for a local
maximum to determine the instance when the bag was starting to be
released, bag-leaving-hand. The height of the hand was then inspected
for the lowest value and this instance was deemed bag-fully-on-
ground.
Separate load timing vectors were then created for the hands and
shoulders from these load timing instances to create smooth load
transitions. From bag-contact to full-bag-support the hand loading
vector was linearly increased from 0 to 1, where at 1 the load had been
fully transferred to the hand. During sagittal plane box lifts, hand force
loading has been shown to increase approximately linearly in instances
where the subject does not push down on the box prior to pick up as
would be the case for lighter loads (Eger & Stevenson, 2004). In the
case of shoulder carries, full-bag-support was maintained in the hand
until the load was transferred to the shoulder. The load in the hand is
decreased linearly while the load on the shoulder increased linearly
during the 3/10ths of a second after the hand-to-shoulder transfer. The
transfer length of 3/10ths of a second was chosen during pilot testing
because the transfer needed to occur in a relatively short period of
time around the hand-to-shoulder transfer instance. Had we elected to
transfer the load over a longer period of time we might have observed
that some of the load was placed in the hand while it was not near the
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shoulder. During the carry phase, the load was either fully in the hand
or on the shoulder.
The process used to determine the loading vector when picking up the
bag was reversed when putting the bag down. For hand carries the
load was maintained until bag-leaving-hand and was then decreased
linearly until bag-fully-on-ground. For shoulder carries, the load was
transferred to the hand at the hand-to-shoulder-transfer and then
decreased until bag-fully-on-ground.
Specific body segment parameters are then added to the model using
subject height, weight, gender, and anthropometric information.
Moment of inertia and centre of mass values are determined based on
Zatsiorskys anthropometrics tables (Zatsiorsky 2002, pp. 304-305,
The Inertial Characteristics of Human Body Segments of 100 Male
Subjects).
The predicted segment endpoint positions were filtered using a
second-order low-pass Butterworth filter with cutoff frequency of 6 Hz
(Dickerson, Hughes & Chaffin, 2008). Segment centre of mass (COM)
positions were determined as a percentage distance between proximal
and distal segment endpoints (Zatsiorsky 2002). Linear COM velocities
and accelerations were calculated from the COM displacements, while
angular velocities and accelerations were calculated by successive
numerical differentiation of the segments angular orientation.
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Using the anatomical model and load timing vectors, a three-
dimensional hands-down, inverse dynamic model calculated the forces
and moments at the L5/S1 joint. These calculations are based on Hof
(1992).
Once the net external moment was computed, a single back extensor
model was used to calculate the force required by the spinal extensors
to balance the external moment. The required force was calculated by
dividing the external moment by a spinal extensor moment arm length
of 6 cm (Norman et al. 1998) and the resultant force was used to
determine the final forces on the L4/5 disc. For a complete breakdown
of the calculations see Appendix A.
The entire process to produce compressive and AP shear force
estimates is presented below.
Figure 6 - Flow chart representing process by which compressiveand AP force estimates are produced. Anthropometric data andoptimization trials are used to produce an optimized participantspecific link segment model. The participant specific LSM is thenused as input to the 3D inverse dynamic model with the data fromeach lifting trial and the generated load timing vectors. Thisproduced compressive and AP shear force estimates for each trial.
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3.4 Methods
A comparison was required against a validated model to ensure that
our model could accurately determine the joint loading during the
lifting tasks. For this comparison we used the 3-Dimensional Static
Strength Prediction Program (3DSSPP) developed by the University of
Michigan. 3DSSPP is a software program that uses postural
information, relevant anthropometric variables and force input to
determine joint forces during a task. The 3DSSPPs output includes:
compressive forces and moments at the L5/S1 joint, shoulders, elbows,
and hands, as well as reports predicting the percentage of the
population that are able to safely complete the task. 3DSSPP is unable
to incorporate dynamic components of lifting and is accurate when the
rate of lifting is below 3 Hz. 3DSPP has been validated as a strength
prediction assessment tool with a correlation greater than 0.85 with
actual strength data; however, it is sensitive to postural input errors
(Chaffin & Erig 1991; Chaffin 1992).
A series of lifting trials was simultaneously recorded using 3DSSPP and
our model. These trials consisted of a simple box lift requiring the
subject to pick a 10kg box off of a table, touching it to the ground and
returning the box to its original position on the table. Two versions of
our model are presented, a fully dynamic model that we used for
paramedic testing and a 3DSSPP-matched static model. This was
necessary since 3DSSPP is a static model and the segment lengths it
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uses are based on the entered subject height and cannot be
manipulated individually.
3.5 Results
Figure 7 - Presentation of the compressive forces on the L5/S1 jointas a result of a stoop lift as calculated by 3DSSPP, a static-linkedsegment model designed to match 3DSSPP and a fully dynamic linksegment model.
As can be seen in Figure 7 through 10 the 3DSSPP matched link
segment model follows the 3DSSPP curve for compressive and
anterior-posterior (AP) shear forces. This is particularly critical as these
are two of the main outcome measures of this study. One area of
obvious disagreement between 3DSSPP and our LSM is in the middle of
the trial, when the participant is placing the box on the ground. This is
only the case for compressive force estimates as AP shear forces
estimates seem to be in close agreement throughout the entire trial.
During the middle of the trial the subject is squatting down and
touching the box to the ground. At this instance we should see slightly
lower compressive force estimates than at pickup as the weight is held
closer to the body and the participant is no longer reaching forward.
The difficulty in matching the mannequin in 3DSSPP to the observed
posture could account for some of this error.
Figure 8 - Presentation of the compressive forces on the L5/S1 jointas a result of a squat lift as calculated by 3DSSPP, a static-linked
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segment model designed to match 3DSSPP and a fully dynamic linksegment model.
Figure 9 - Anterior-posterior force on the L5/S1 joint as a result of astoop lift; calculated from 3DSSPP and two link segment models.Positive values represent a tendency to translate anteriorly.
Figure 10 Anterior-posterior force on the L5/S1 joint as a result of asquat lift; calculated from 3DSPP and two link segment models.Positive values represent a tendency to translate anteriorly.
3.6 Discussion
We are pleased with the similarity between the compressive and AP
shear force curves from 3DSSPP, the 3DSSPP-matched static link
segment model and the dynamic link segment model. One obvious
disagreement between 3DSSP and our models occurs near the middle
of all trials; this is the instance when the box is closest to the ground.
We hypothesize that the difference in this phase of the lift is due to
how each model represents the back. 3DSSP has a single segment
representing the entire back while our model uses two segments;
3DSSPP therefore does not consider thoracic flexion and as such some
information may be lost.
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In the case of the stoop lift, the maximum forward flexion measured in
our model was approximately 100 of flexion for the lumbar segment
and 140 for the thoracic segment, while 3DSSPP measured 100 of
flexion for the single trunk segment. For our model, the additional 40
of forward flexion in the upper trunk causes a shorter moment arm
thereby reducing the external moment. Therefore, the reaction
moment that is required to balance the system is lower and leads to
lower force estimates. The differences can be seen in Figure 11, as the
participant rounds out their shoulders when touching the box to the
ground.
Figure 11 - Three representations of the same position of a trial. Onthe left the participant touching a 10kg box to the ground as part ofa stoop lift. In the centre the same image overlaid with a mannequinfrom 3DSSPP. On the right the Link Segment Model.
In Figure 11 it is apparent how the differences between the flexion
angles come about. The upper back sensor on the participant is tipped
very far forward and may be in excess of the actual flexion of the
thoracic segment. The 3DSSPP model has no forward flexion at this
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point and, as a result, the mannequin is closer to 100 of flexion
requiring a greater muscular effort to balance the system than the
140 seen in the link-segment model. It would seem that the flexion of
the thoracic segment that 3DSSPP lacks may be driving some of the
differences between the models.
In order to understand how differences in back representations would
influence paramedic research we calculated the average flexion angle
at the peak loading instance for all paramedic trials. At peak loading no
participant was flexed more than 80 so we compared 3DSSP and our
LSM during a stoop and squat lift when both models were at 80. Most
participants average peak flexion was around 60 so we compared our
model against 3DSSPP at this angle as well.
Figure 12 - Compressive and AP shear force LSM estimates of astoop and squat lift at 60, 80, and 100 degrees of upper back flexion
represented as a percentage of those values found using 3DSSPP.
As trunk flexion increases past 60 degrees our dynamic LSM force
estimates and 3DSSPP force estimates begin to diverge. As flexion
reaches 100 degrees this difference reaches almost a 1000N
discrepancy. However, we believe that the true compressive force
value lies somewhere between our value and 3DSSPPs at this angle as
trunk flexion, especially thoracic flexion, appears decreased in the
3DSSPP model but increased in ours as a result of the shoulders
rounding out. Given that most paramedics were not required to go
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into high degrees of trunk flexion this is less of an issue for our
research than the stoop and squat lift comparisons present.
Additionally, the high degree of agreement (greater than 80% for
much of the trial) between 3DSSPP and our model shows that we can
as validly represent human motion and calculate the resultant forces
within the range of 0-60 of forward flexion.
It appears that sensor placement can be an issue when it comes
estimating low back loading especially in the case of large thoracic
flexion such as when touching the ground. If the sensor is placed too
low on the thoracic spine then thoracic flexion is underestimated, but if
the sensor is placed too high then flexion is overestimated. This may
provide a direction for future research to determine the best sensor
location for the thoracic segment to achieve best estimates of thoracic
flexion.
We have shown that our link-segment model is a valid tool to represent
human motion. We also believe that our novel shoulder model is more
appropriate than static shoulder models in reflecting shoulder joint
translation. If we assume that our shoulder model more truly
represents what actually happens, and this is then used as input for a
dynamic inverse model, then we should expect that more valid loading
estimates are the result.
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Chapter 4: Paramedic Lifting
4.1 Introduction
The 72 certified land ambulance services in Ontario respond to an
estimated 1.5 million calls annually. In responding to these calls
paramedics carry their equipment bags over long distances, up and
down stairs, and through confined spaces. Given that paramedics can
respond to a number of calls per shift it is not surprising that the
various types, size, and weights of these bags are a concern. Despite
the fact that common equipment is carried in the bags, there are no
standards governing the size and number of bags or the weight carried
in any particular bag. The Association of Municipal Emergency Medical
Services of Ontario (AMEMSO) as part of its responsibilities to its
members, wanted to investigate bag lifting as a possible cause for
concern regarding low back injury rates.
In addition to developing contacts with regional paramedic services,
initial meetings were scheduled with AMEMSO President Paul
Charbonneau to increase our understanding of the paramedic
population based on his opinion of: number of hours worked per week,
number of calls per shift, shift timing, general fitness of paramedics,
general attitude of paramedics, speed of work, and general complaints
made by paramedics regarding their working conditions. These
meetings lead to visits to paramedic services in Kingston, Waterloo,
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Sudbury-Manitoulin and Greater Sudbury during the month of March to
record the range of bag weights, designs and lifting styles.
Prior to collecting information, letters of understanding and permission
were signed by a representative from each participating service as well
as all participating paramedics, as approved by the Queens General
Research Ethics Board. Paramedics were asked to fill out a
questionnaire as well as answer some questions during an informal
interview. During these interviews paramedics were asked which
aspects of bag lifting they felt were most demanding, as well as how
they felt bag lifting ranked in relation to other demanding tasks
associated with the job. Furthermore, some services participated in
pilot lifting trials on-site similar to those performed later in the lab.
These pilot testing sessions were used to get a sense of the types of
motions performed by paramedics as well as learn if the paramedics
felt comfortable performing the testing protocol. In total eight subjects
participated in pilot testing and 16 participated in interviews or filled
out questionnaires.
These meetings and pilot testing sessions, along with information
gained from a questionnaire sent out to paramedics by Dr. Renee
McPhee (personel communication), led to an understanding of the
general working demands of paramedics across the province.
It was found that:
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Paramedics frequently carry bags ranging from 1-20kg, with most
bags in the range of 5-12kg.
When responding to calls usually more than one bag is carried.
When loading out, bags are often picked up off the stretcher at
about waist height, carried to the person in need and then placed
on the ground.
When loading in, bags are picked up off the ground then placed on
the stretcher or carried by the paramedic.
Heavy bags and bags requiring awkward postures were the biggest
complaint, partially due to uneven loading in the bags or carrying
too much equipment, some of which is rarely used.
Two common suggestions were received: reducing the amount of
weight in the bags and move towards backpack style bags when
possible. One participant suggested the use of wheelie bags to
reduce the demand on paramedics.
These insights led to the development of an in-lab testing protocol that
was designed to reflect the bag lifting demand experienced by
paramedics.
4.2 Review
Lifting has been identified as a hazard that can lead to low back pain
(Marras et al. 1995; Chaffin & Park 1973). Paramedics share similar
lifting task demands with a variety of professions such as nurses,
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nursing aides, and fire-fighters who are required to support a patient
and administer some level of care while simultaneously transporting
the tools necessary to provide that care. Studies of the nursing
population have shown high levels of low-back pain much of which is
related to patient handling, (Jensen 1987, Videman 1984) a task that
paramedics are frequently required to perform with, potentially,
greater strain due to awkward postures and lower lift origins.
Back injuries are very common in the paramedic population accounting
for 36% of total injuries; of those more than half were caused by lifting
activities (Hogya & Ellis 1990). Crill & Hostler (2005) surveyed EMS
providers and found that almost 20% had reported a back injury while
performing EMS work in the previous six months. It is important to
understand which aspects of paramedic work lead to low back pain.
Measures that have been correlated with increased low back pain
include: peak compressive force, and peak shear force experienced by
the L4/L5 or L5/S1 joint, (Norman et al. 1998; van Dien & Toussaint
1997). The National Institute for Occupational Safety and Health has
identified two limits for compressive force on the lower back based on
biomechanical, psychosocial, and physiological research. 3400N has
been put forward as an acceptable limit below which most workers
should be able to work safely for long periods of time without injury
(NIOSH 1981). A maximum permissible limit of 6400N was established
beyond which individuals cannot work without injury. Waters et al
(1993) argue that if the data were normally distributed 21%-30% of
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lumbar segments would fail when loaded with a force of 3400N given
the ultimate compressive force values put forward by Brinckmann et al
(1988) and Jger & Luttman (1989). The limit of 3400N is a
conservative estimate for a healthy working population since cadaver
lumbar segments may have lower tolerance limits (Hansson & Roos
1981); however, when ensuring workplace safety conservative limits
should be used.
Shear forces act in the medial-lateral and anterioposterior directions
along the spine and are a risk factor for low back pain (Kerr et al.
2001). Krypton et al. (1995) found shear force tolerance limits in
cadavers of between 1700N and 2900N. While less work has been
done in this area, some guidelines are based on thinking similar to that
of the NIOSH Equation. McGill et al. (1998) put forward an action limit
of 500N and a maximum permissible limit of 1000N. A shear force limit
of 500N has been used with reasonably accuracy to predict which
workers reported low back pain (Daynard et al. 2001). Motions that
keep compressive forces below 3400N and shear forces below 500N
are unlikely to increase the risk of injuries.
Clearly, it is important to understand the loads generated by
paramedics across a variety of lifting conditions. An understanding of
the magnitude of these loads while performing paramedic work is
essential to determine if the weights and/or lifting techniques are safe.
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4.3 Methods
4.3.1 Participants
Twenty-five participants were recruited from the Kingston paramedic
community. All volunteers reported no low back, shoulder, arm, hand
and wrist pain in the last year. Informed consent, approved by the
Queens University General Research Ethics Board, (Appendix B) was
given before testing. Participants were provided a $50 honorarium to
compensate for travel and parking.
4.3.2Instrumentation
Prior to testing, subjects were outfitted with the Xsens Motion Tracking
System, a wireless motion tracking system with sensors that combine
accelerometers, gyroscopes and magnetometers to determine a
sensors orientation. Due to hardware difficulties that caused two
sensors to be unusable and that no additional sensors were available,
the instrumentation setup was altered from that used in model
development. It was decided that the scapular sensors were the least
important since their range of motion was the smallest. As a result only
6 sensors were used for paramedic testing.
Data obtained from pilot testing was reevaluated by reducing the
number of sensors used in creating the link-segment model, to test the
influence of the novel link-segment model on force outputs during
lifting and carrying tasks. Visual inspection showed of force curves
showed changes of less than 10% when removing the scapular
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sensors. As a result we could assume that our model was still valid
when using only 6 sensors.
Subjects were instrumented with sensors placed on the lower back,
upper back, upper arms, and lower arms. When attached to the
aforementioned limbs the motion of the participant can be tracked.
Instead of determining clavicular orientation using Xsens sensors, the
clavicle segment was projected straight out from the upper back
segment based on the direction of the medial-lateral axis of the upper
back segment. The clavicle, upper arm, and lower arm segments were
all optimized in the same way as explained in Section 3.3.3.
4.3.3Bags
Four EMS bags (provided by the Frontenac Paramedic Service) were
acquired and loaded with weights of 5kg, 10kg, 15kg and 20kg. The
5kg and 10kg bags were carried as handbags, the 15kg bag was
carried as a shoulder bag, and a backpack was loaded with 20kg.
These bags are shown in Figure 13.
Figure 13 - EMS bags used during in-lab testing
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4.3.4 Lifting Tasks
Two conditions were developed for the 5kg and 10kg bags, the first
condition had the bag placed on the edge of a table as close to the
participant as possible while in the second condition the bag was
placed approximately 50cm away from the edge of a 78 cm high table,
requiring paramedics to reach forward. A height of 78 cm was used as
it was between the height of the stretcher and back of a paramedic
SUV from which paramedics lift their bags. Two conditions were also
developed for the 20kg backpack; the first required the participant to
pick up the bag and sling it over one shoulder; the second required the
participant to carry the bags like a normal backpack (i.e. using both
shoulder straps). In total there were seven lifting conditions (two each
for 5kg, 10kg and 20kg bags, one for 15kg bag). For most lifting
conditions the participant picked the bag up off of a 78cm high desk,
carried it 4m and placed it on the ground. This mimics arriving at the
scene, removing the bag from the back of the vehicle or off of a
stretcher and carrying it to the site. The exception to this was the
double strap shoulder backpack carry where the experimenter helped
the subject load the backpack onto their shoulders, which avoided
disrupting the sensors placed on the subjects back.
Simulating carrying the bags back to the ambulance was not done. A
list of these conditions is presented in Table 1.
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Table 1 - Conditions presented to paramedics during in-lab testing.All conditions included a 4m carry.
2
Bag Bag Size
HxWxD
(cm)
Weight
(kg)
Lift Type LiftOriginHeight
ForwardReach
Distance
LiftDestinat
n
YellowALS Bag 23 x 32 x 12 5 Hand
78cm 0cm Floor
78cm 50cm Floor
BlueOXYGEN
Bag33 x 58 x 24 10 Hand
78cm 0cm Floor
78cm 50cm Floor
RedDuffelBag
28 x 52 x 34 15Single Strap
Shoulder78cm 0 cm Floor
OrangeBackpac
k55 x35 x20 20
Single StrapShoulder
78cm 0cm Floor
DoubleStrap
N/A N/A N/A
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4.3.5 Data Collection
Each subject was asked to perform each condition four times,
producing 28 trials per subject. Due to a combination of hardware and
software problems four subjects performed fewer than 28 trials. In total
21 subjects completed all 28 trials, while all 25 subjects completed a
minimum of 22 trials. The participants height, weight, gender and
relevant anthropometric measurements were recorded after testing.
Participant anthropometrics are listed in Appendix C. Sensor
orientation was recorded using MT Manager software (Xsens
Technologies, Netherlands). Each trial was recorded and exported
individually before analysis.
Figure 14 - Representation of trial setup. Participants were requiredto lift a bag from a height of 78 cm, at a reach distance of 0 cm or50cm, and then carry it 4m before placing it on the ground.
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After testing, participants were asked to comment on how closely the
lifting protocol reflected their day-to-day lifting tasks. Most participants
said that the general lifting requirements were accurately represented;
however, they felt that the wide variety of lifting demands (i.e. stairs,
small doorways, multiple bags) were not present in testing (for all
responses see Appendix D).
4.3.6 Data Processing & Statistical Analysis
All trials for all subjects were visually inspected to determine load
timing phases and create loading vectors as explained in Chapter
3.3.4. All data processing was performed in Matlab (R2009a, The
MathWorks). The output from each trial was time-normalized to
create load timing phase consistency across trials and subjects through
piecewise normalization. In this process each phase of the trial is
allotted a specific percentage of the trial and is linearly interpolated
between relevant load timing instances. This process allows
subsequent ensemble averaging the phases without any phase shifts.
For statistical analysis, relevant data were extracted from curve
profiles for each trial. These were: peak compressive force at bag
pickup, trunk lean at bag pickup, forward reach at bag pickup, shoulde