robots for neurorehabilitation and assistance of gait
TRANSCRIPT
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Prof. Dr.-Ing. Robert Riener Sensory-Motor Systems Lab Institute of Robotics, ETH Zurich University Hospital Balgrist, University of Zurich
Robots for Neurorehabilitation and Assistance of Gait
Neurotechnix 2015 Lisabon 16/17 November 2015
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SMS Lab: 2 Affiliations, 2 Locations
ETH Zurich University Hospital Balgrist
Spinal Cord Injury Center Institute for Robotics and Intelligent Systems
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Patient Activity: Swiss Clinics
Kool, Wieser, Brügger, Dettling. Entwicklung eines Patientenklassifikationssystems (PCS) für die Rehabilitation in der Schweiz, ZHAW, 2009
Clin
ic
Mean Occu-
pational
Physio
Physicians
Care
less than 2 hours per day => less than 6 % motion therapy
min
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Patient Activity
More than 90% of the time inactive
www.fokus.de
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Infants Practice a Lot • 0 to 12 month-olds are about
33% of their time active (estimated from Iglowstein et al. 2003)
• 12 to 19 month-olds perform about 420’000 million steps in a month when learning to walk (Adolph et al. 2012)
Children Activity
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For the Therapist • Physically exhausting • Ergonomically
inconvenient
For the Patient • Limited training duration • Gait pattern not optimal
SCI Center, Balgrist University Hospital, Zurich
Disadvantage of Manual Training
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Current Robotic Plattforms
for Neurorehabilitation
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Lopes GaitTrainer
Haptic Walker
G-EO
Lokomat
Autoambulator
Gait Rehabilitation "Robots"
Alex
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Lokomat G. Colombo V. Dietz Balgrist, Hocoma AG
Robot-Aided Gait Training
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Lokomat with 7 Degrees of Freedom
Robot-Aided Gait Training
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Robot-Aided Gait Training How a Future Lokomat Could Look Like!?
G. Colombo (Hocoma AG), A. Luft (University of Zurich); R. Riener (ETH Zurich), H. Vallery (TU Delft, ETH Zurich)
1 Body weight support system
2 Pelvis module
3 Exoskeleton
4 Footplates
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Robot-Aided Gait Training
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Human-Robot Cooperation
Human Machine
Interaction, Cooperation
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Human-Robot Cooperation
Increase Intensity •Duration x frequency
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Human-Robot Cooperation
Increase Intensity • No. of repetitions
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Human-Robot Cooperation
Increase Intensity • No. of repetitions • Physical effort
- Strength
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MR-Compatible Stepper
Collaborations: R. Riener, S. Kollias, V. Dietz
Active vs Passive Movements
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Active vs Passive Leg Movements 1 Heathy Subject, 21 s stepping
→ Stronger activations during active movements
Contrast active leg movements versus rest
a)
R
Contrast passive leg movements versus rest
b)
R
Collaboration: S. Kollias, V. Dietz
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Human-Robot Cooperation
Increase Intensity • No. of repetitions • Physical effort
- Strength - Assist as needed
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Assist-as-Needed (AAN) with SCI Mice Animals • 27 mice, 14 days post SCI Training • Admin of serotonin agonist • 30 sessions 10 min each
(6 weeks, 5 per week)
Cai et al. 2006
“band group” “window group” Results • AAN with window paradigm shows highest level of recovery
with respect to step number, periodicity and consistency.
Three Control Strategies • Fixed movement • AAN training with different
hindlimb coordinations
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Mechanical Interaction
Path Control • Robot behaves assistive,
corrective or transparent, when needed
• Free timing for patient
• Support patient, but do not restrict patient
Duschau-Wicke, Vallery, Riener, et al.
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Pos Path_a
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Hfrz
*
Heart Rate
Pos.contr. Path contr.
Muscle Activity
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
Nor
mal
ized
mus
cle
activ
ity (B
F)
Position control Path control
Rela
tive
incr
ease
of h
eart
rate
11 incomplete SCI subjects
Path Control Increases Participation
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Path Control Enhances Variability
Position Control
-20 -10 0 10 20 30 40 0
10
20
30
40
50
60
70
80
Hip angle [°]
Kne
e an
gle
[°]
Path Control
-20 -10 0 10 20 30 40 0
10
20
30
40
50
60
70
80
Hip angle [°]
Kne
e an
gle
[°]
“Repetition without Repetition” (Bernstein)
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RIC Single Case Study
Subject • 52 year old male • Right sided stroke • 7 months post-stroke
Lokomat Training • 12 training sessions
(4 weeks, 3 per week)
Features • Patient cooperative strategy: path controller • Ankle trajectory tracking: Follow a displayed target template
leading to increased hip and knee flexion during swing phase
Krishnan et al. 2012
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Training Improves Ankle Tracking Kinematics
RIC Single Case Study: Results
Reduction of ankle kinematic variability
Improvement of ankle tracking performance
Krishnan et al. 2012
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Performance Variable Pre-training Post-training Follow-up
TUG 14 s 11 s 11 s
6-min Walk 228 m 316 m 304 m
Single-Leg Balance 1 s 15 s 14 s
Self-Selected Gait Velocity
0.72 m/s 1.0 m/s 0.85 m/s
Fast Gait Velocity 1.1 m/s 1.3 m/s 1.3 m/s
LE Fugl-Meyer 16 20 23
RIC Single Case Study: Results Training Improves Clinical Outcome Krishnan et al. 2012
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Improvements Are Substantially Larger • than mean improvement seen after conventional Lokomat
(i.e. position-controlled) assisted gait therapy and • than manual therapist-assisted treadmill training Hornby et al. 2008; Westlake & Patten 2009; Husemann et al. 2007;
Plummer et al. 2007
RIC Single Case Study: Results
Results Could Be Repeated with a 2nd Case Krishnan et al. 2013 (Archives of PM&R)
Clinical Trials with Cooperative Training Modes Required
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Conventional Training
Can be monotonous and boring
Motivation During Gait Training
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Human-Robot Cooperation
Increase Intensity • No. of repetitions • Physical effort
- Strength - Assist as needed
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Human-Robot Cooperation
Increase Intensity • No. of repetitions • Physical effort
- Strength - Assist as needed
• Mental effort - Task & difficulty - Motivation & reward
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Gait Training & Virtual Reality
Collaboration: Children Hospital Affoltern, Hocoma, ZHDK
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Neuro- plastic effects
Neuro- plastic effects
Increased neuro- plastic effects
Engagement Increases Neuroplasticity
Engagement
Physical activity
Mental activity
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Yerkes-Dodson‘s Law
weak engagement optimal engagement
exhaustion
Engagement
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Increasing Arousal
Hocoma & ETH Zurich
Audiovisual Scenarios
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Audiovision
Robot & Treadmill
Control Mental Engagement
Position & Force Sensors
Patient Status
Biomechanical Status
Motions & Forces
Cooperative Controller
Position & Force Sensors
Physiological Signals Physiologische Sensoren
State Estimator
Linear Discriminant
Analysis
Kalman Filtering
Task Performance
Psychophysiological Control Loop A. Koenig, R. Riener (ETHZ) & M. Munih, D. Novak (Univ. Ljubljana)
Desired Engagement
- bored - challenged - over- stressed - normal
Rendering
Audiovisual Scenery
Task Difficulty
Biomechanics
Biomechanical Control Loop
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Rewalk Rexbionics
Cyberdyne
Parker
EksoBionics Honda
Park et al., 2011
Zoss et al., 2006
Wehner et al., 2013
NASA MIT Exos
Raj et al. 2011
Portable Gait Exoskeletons
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Advantages and Disadvantages
• Motion assistance or strong guidance
• Support of body weight • Enable different motion
tasks
• Added mass, inertia, friction • Limited transparency • Kinematic constraints • Bulky device • Collisions with environment • Discomfort
Wearing a Rigid Exoskeleton Means …
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Versatile Lower Limb Exoskeleton A tool to investigate the influence of exoskeleton design characteristics on human-robot interaction
VLEXO
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VLEXO: 8 DOF with large ROMs
Passive Device!
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Design Variables 1. Kinematic contraints
- limited number of DOFs - reduced ROM
2. Joint axes misalignments 3. Exoskeleton mass 4. Exoskeleton backlash
VLEXO: Effects to Investigate
Effects A. Interaction forces between
exoskeleton and human B. Movement alterations,
e.g. at trunk and ankle C. (Dis-)Comfort, pain D. Exoskeleton sensing
inaccuracies
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Mass Properties • Low mass of the device
"maximal 6 kg at the pelvis and 2 kg at the ankle of additional INERTIA no significant gait alteration in healthy people" (Meulemann et al. 2013)
• Additional mass can be added to investigate effects
Variable Exoskeleton Mass
3.5 kg
1.5 kg
1.8 kg
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Variable Misalignment
Exoskeleton Human
𝛿axis
Human
𝛿𝑚𝑚𝑚
𝜙joint
Exoskeleton
𝐹mis = 𝑓(𝜙joint, 𝛿axis, 𝑥cuff)
𝑐𝑚𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝑥cuff
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Effects on Interaction Forces
1-DOF force sensors at each cuff attachment
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Soft vs. Stiff Exoskeletons
Advantages • Less bulky • Larger ROMs • Less misalignment • Lower mass • Higher comfort
Wyss-Institute, Boston (Asbeck, Alan, Walsh, et al.)
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Our Target Scenario
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Target Population • Elderly and neurological patients
with residual muscle function • Wheelchair users
Target Movements
Target Scenario
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Which Joints?
Sagittal plane • Hip joint • Knee joint • Ankle joint • Combination of joints
Simple Design • Restrict to sagittal plane • Only 1 active DOF per leg
(cable system) • Exploit joint synergies
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Walking: Terminal-Stance
LR TSt
Main Joint Actions • Hip flexion • Little knee torque • Ankle plantarflexion
Accelerating Action!
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Walking: Loading Response (& MSt)
LR
Main Joint Actions • Hip extension • Knee extension • Little ankle torque
MS
Antigravity Action!
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Antigravity vs Accelerating Support
Main Joint Actions • Hip extension • Knee extension • Little ankle torque
Antigravity Action!
Main Joint Actions • Hip flexion • Little knee torque • Ankle plantarflexion
Accelerating Action!
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SMS Exosuit - Concept
MCU
Actuation Path
Passive Element
Tendon Actuator
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SMS Exosuit - Concept
MCU
Actuation Path
Passive Element
Tendon Actuator
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Stiffness of the Suit-Human-Interface
Woven Knitted Non-Woven
Textile Architecture
Textile Materials Human Anatomy and Tissue
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Tendon Actuator
Pulley System
Specifications Max. force: 700N Max. cable travel: 24cm Weight: 650g
Tendon Connector
Brushless Motor
Load Cell
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Thigh Interface
First Layer: Stiffness • Carbon, glass fiber and polyamide • On crucial spots of actuation path • Entire suit remains soft
Second Layer: Actuation • Holds actuators • Guides tendons along the
human body
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Anchoring Points
Pelvis Anchorig Point
Foot anchoring point
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Passive Elements Passive Antagonists • Bio-inspired, antagonistic
architecture • Increases joint stability • Supports hip & knee flexion
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SMS Exosuit – Integrated Sensors
Knee and hip joint angles: String potentiometers
CoM velocities: Accelerometer
Force change for sit-to-stand: FSRs (seat switch)
Gait phases: FSRs Gyroscopes
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MCU and Battery Placement
Batterie compartment
MCU
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Sit-to-Stand Transfer
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Stair Ascent
MAXX: Mobility Assisting Textile Exosuit
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CYBATHLON
CHAMPIONSHIP FOR ROBOT-ASSISTED ATHLETES
WITH DISABILITIES
CYBATHLON
CHAMPIONSHIP FOR ROBOT-ASSISTED ATHLETES
WITH DISABILITIES
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«Walk Again» Project, M, Nicolelis et al. FIFA World Cup 2014
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1World Health Organization. Guidelines on the Provision of Manual Wheelchairs. WHO Press, 2008.
65 Million Wheelchair Users Worldwide1
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C-Leg Otto Bock
60’000 C-Legs sold till 2015
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Common Prostheses Are Not Actuated
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Breaking News, USA 11/2012 © Reuters
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Goal of the Cybathlon
Promote the Development of USEFUL Assistive Systems
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… and Remove Barriers between
People with Disabilities
Research and Development
General Public
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Swiss Arena Kloten, Zurich
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Powered Exoskeleton Race
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Powered Exoskeleton Race
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Powered Leg Prosthesis Race
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Powered Wheelchair
Race
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Powered Arm Prosthesis Race
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Muscle Stimulation Bike Race
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Start 2
Goal 2
Goal 1
Start 1
5 rounds
Muscle Stimulation Bike Race
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Brain-Computer Interface Race
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Brain-Computer-Interface Race
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Zeit BCI Station FES Bahn Objektbahnen 10:00 ARM-Q1 10:10 ARM-Q2 10:20 ARM-Q3 10:30 FES-Q1 10:40 BCI-Q1 10:50 FES-Q2 11:00 LEG-Q1 11:10 LEG-Q2 11:20 LEG-Q3 11:30 FES-Q3 11:40 BCI-Q2 11:50 FES-Q4 12:00 EXO-Q1 12:10 EXO-Q2 12:20 EXO-Q3 12:30 FES-Q5 12:40 BCI-Q3 12:50 FES-Q6 13:00 WHEEL-Q1 13:10 WHEEL-Q2 13:20 WHEEL-Q3 13:30
Begrüssung Show
13:40 13:50
Zeit BCI Station FES Bahn Objektbahnen Zeremonien 14:00 FES-F-D 14:10 BCI-F-B 14:20 FES-F-C 14:30 ARM-F-B 14:40 ARM-F-A 14:50 BCI-F-A 15:00 ARM-Race 15:10 FES-F-B 15:20 BCI-Race 15:30 FES-F-A 15:40 FES-Race 15:50 LEG-F-B 16:00 LEG-F-A 16:10 LEG-Race 16:20 EXO-F-A 16:30 EXO-F-B 16:40 EXO-Race 16:50 WHEEL-F-B 17:00 WHEEL-F-A 17:10 WHEEL-Race 17:20
Abschlussrede Show bis ca. 19.00
QUALIFICATIONS: Morning FINALS: Afternoon
LIVE TV
Cybathlon: 8 October 2016
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Discipline Pilots
FES Bike 11 (+3)
Leg Prosthesis
8 (+1)
Exoskeleton 16 (+2)
Wheelchair 8 (+1)
Arm Prosthesis
7 (+1)
BCI 15 (+3)
Actual State (Sept. ‘15) • 65 (+11) Pilots • 55 Teams • 22 Countries:
- Europe (CH, UK, BE, FR, DE, ES, AT, SW, IS, etc.) - North America (US, CA) - Latin America (MX, BR) - Asia (JP, KO, HK, SP, TH) - Australia
Registrations: Current State
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Japanese Television Shows
NHK Sakidori, June 2015 NHK News Channel, Oct 2014