paradigm shift in neuroscience - barrow neurological institute€¦ · 65 15 10 t t s t wrist speed...
TRANSCRIPT
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Hermano Igo Krebs
IEEE Fellow
Barrow Sep 2016
MassachusettsInstitute ofTechnology
Disclosure: Dr. H. I. Krebs is co‐inventor in the MIT‐held patents for the robotic devices used to treat patients in this work. He holds equity positions in Bionik Laboratories, Watertown, MA – a company that manufacturers this type of technology under license to MIT.”
Paradigm Shift in Neuroscience
Myth and Legend:
Traditional care assumes that brain is hardwired and cannotrecover once sensorimotor areas are destroyed
Reality:
New understanding: after stroke and other neurological injuries plasticity occurs and might accounts for remapping of new pathways
Hardwired Since the 1928 work of Santiago Ramón y Cajal, famed neuroscientist, the prevailing assumption has been that the central nervous system is hardwired, non‐malleable, and incapable of repairing itself.
Clinicians have selected compensation as a rehabilitation strategy for non‐remediable deficits of strength, voluntary motor control, sensation, and balance.
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Rationale for Restorative Efforts and Training in Stroke Recovery: Animal Models for Activity Dependent Plasticity
Nudoet al.,Science; 1996
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Paradigm Shift in Neuroscience
Myth and Legend:
Traditional care assumes that brain is hardwired and cannotrecover once sensorimotor areas are destroyed
Reality:
New understanding: after stroke and other neurological injuries plasticity occurs and might accounts for remapping of new pathways
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https://en.wikipedia.org/wiki/Spinal_locomotion
2010 Guidelines
Outpatient & Chronic
Inpatient
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2010Guidelines
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2016 Guidelines
2016 AHA
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LE Robotics
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RCTs: ambulatory chronic and subacute stroke
50
40
30
20
10
60
0
50
40
30
20
10
60
0
post-4 weeks 6 month follow-up post-4 weeks 6 month follow-up
Therapist-assisted
Robotic-assisted
* **
*
0
0.1
0.2
0.3
Mid Post Follow-Up
Ch
an
ge
in S
pe
ed
(m
/s)
*
*
*
0
100
200
300
400
Mid Post Follow-Up
Ch
ang
e in
Dis
tan
ce (
ft)
*
*
Gait speed 6 min walk
Severely impaired (< 0.5 m/s) Mod impaired (0.5‐0.8 m/s) Chronic hemiparesis Robotic‐ vs. therapist‐
assisted training 4 weeks (3X/wk, 30
min sessions, 6 mo. follow‐up
Subacute hemiparesis (< 6 mo) Robotic vs.
conventional (focused nearly entirely on gait training)
8 weeks, 3 mo follow‐up
Hornby et al Stroke 2008, Hidler et al NNR 2009
Conventional physical therapy
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Body weight support treadmill training (BWSTT) for SCI
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Interventions1.5 hrs, 3x/wk, 12 wks, structured & progressive programs (courtesy Pam Duncan)
Locomotor Training Program Home Exercise Program
• 20‐30 min at 2 mph on TM with BWS
• Progression: endurance, speed, BWS, independence, adaptability
• Followed by walking practice off the treadmill
• 2‐3:1 therapist/patient
• Strength exercises
• Balance exercises
• Progression: repetitions, activity, balance challenge, resistance
• Encouragement to walk daily
• 1:1 therapist/patient
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LEAPS Study
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LEAPS Study
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LEAPS Study
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Translating Neuroscience into Practices: “Virtual Trajectory” Submovements
Oscillations
Impedances
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Non‐Human Primates
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Virtual Trajectory
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Train monkeys
Cut sensory info
Move the initialHand position
Actual handpath
Finalposition
Initialposition
Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Speed‐Accuracy Trade‐off (Fitts’ Law)
the time, MT, required to complete a discrete movement over different distances, D, to targets of different size, W, and the difficulty of the task, measured by the index of difficulty, ID, in bits as a logarithmic ratio of D to W.
The intercept a can be thought of as an indicator of the reaction time and the slope bas the sensitivity of the motor system to change in difficulty of the task.
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Pediatric Anklebot
Rossi S, Colazza A, Petrarca M, Castelli E, Cappa P, Krebs HI. “Feasibility study of a wearable exoskeleton for children: is the gait altered by adding masses on lower limbs?,” PLOS One 8:9:e73139 (2013). Michmizos K, Rossi S, Castelli E, Cappa P, Krebs HI. "Robot‐Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation." IEEE‐ TNSRE 23:6: 1056‐1067 (2015).
2.5 kg7.21Nm in DP flexion 4.38Nm in IE
25 degrees of dorsi‐flexion,45 degrees of plantar‐flexion, 25 degrees of inversion, 15 degrees of eversion, 15 degrees of internal or external rotation.
Maxon EC‐powermax22‐327739Rolix‐Linear DriveMNS9‐135, SchneebergerGurley with 40960 linesLSB200:00105, 25 lb, Futek
Pointing with the AnkleSpeed‐Accuracy Trade‐off (Fitts’ Law)
Michmizos K, Krebs HI. “Pointing with the Ankle: the Speed‐Accuracy Tradeoff,” Experimental Brain Research, 232:2:647‐657 (2014).
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Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Invariants of Movement
Minimum‐Jerk Speed Profile
3
3
4
4
5
5 10156)(
T
t
T
t
T
tts
Wrist Speed Profile
Vaisman L, Dipietro L, Krebs HI, “A Comparative Analysis of Speed ProfileModels for Wrist Pointing Movements,”IEEE Trans Neural Sys and Rehab Engineering, 21:5:756‐766 (2013).
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AnkleSpeed Profile
0 5 10 15
morassoexpo
mmmsymminsnap
ggedhol
sigcontbeta
mmmasymlgnb
sigdiscontasymgauss
gggengamma
lgnminjerk
symgaussweibullbiexpo
Higher Scores (out of 18) correspond to better fits
Scores are based on 1386 speed profiles
0 200 400 600 800 1000 1200
expoweibull
ggmmmsym
sigcontlgn
asymgausssigdiscont
betalgnb
mmmasymedhol
gammagggen
minjerkmorassominsnap
symgaussbiexpo
# of speed profiles out of 1386 for which a model s fit was among 5 best fits
Michmizos K, Vaisman L, Krebs, HI. "A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are controlled by a Single Invariant Strategy." Frontiers in Human Neuroscience8:962 (2014).
Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Speed‐Accuracy Trade‐off (Fitts’ Law)
the time, MT, required to complete a discrete movement over different distances, D, to targets of different size, W, and the difficulty of the task, measured by the index of difficulty, ID, in bits as a logarithmic ratio of D to W.
The intercept a can be thought of as an indicator of the reaction time and the slope bas the sensitivity of the motor system to change in difficulty of the task.
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Protocol
Michmizos K, Krebs HI. “Reaction Time in Ankle Movements: a Diffusion Model Analysis,” Experimental Brain Research, 232:11:3475‐3488 (2014
Ankle reaction time (Hick‐Hyman law)
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Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Simple arm movements mechanical physics and peripheral neural feedback processes that appear to be continuous‐time phenomena
exhibit behaviors that appear to be characteristic of symbolic or logical processes that operate on primitive units.
appear to involve a mixture of discrete and continuous processes.
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Origins of Intermittency
y-di
rect
ion
(m)
-0.2 -0.1 0 0.1 0.2-0.2
0
0.2
x-direction (m) time (sec)
0 2 4 6 80
0.2
0.4
0.6
spee
d (m
/s)
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Origins of Intermittency Old Conjecture: 1898 Woodworth Intermittency, Submovements, Segments
What is the origin of movement intermittency?
Could it be a fundamental feature of neuromuscular system?
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Krebs, H.I.; Hogan, N.; Aisen, M.L.; Volpe, B.T.; “Quantization of Continuous Arm Movements in Humans with Brain Injury”, Proc. National. Academy of Science;96:4645‐4649, April 1999.
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Origins of Intermittency A) Feedback: Property of visually‐guided motions –delays in visual perception and/or neural transmission
B) Feedforward: occur in all arm motions
C) Low effort execution mode (simple submovements dowloaded as needed)
D) Mechanical
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Test Human Speed Control Experiment:
neurologically unimpaired subjects
constant speed motion
with or without visual display of arm speed
Results: Remarkably poor speed control
fluctuations 75% of mean (±3 limit)
80Km/h (20 and 140km/h)
Worst at slowest speeds
Hypothesis verified
0 5 10 -10
0
10
20
Vel
(de
g/s)
bvj set 3 trial 12
Time (sec)
Vel
(de
g/s)
0 5 10 -10
0
10
20
bvj set 3 trial 13 (BLIND)
Time (sec)
Vis
ion
Blin
d
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Hogan, N.; Doeringer, J.A.; Krebs, H.I.; “Arm Movement Control is both Continuous and Discrete”, Cognitive Studies; Bulletin of the Japanese Cognitive Science Society, 6:3:254‐273, September 1999.
“Quanta” in Recovering Movements
Recovering stroke patient attempting to draw a horizontal circleLeft column: plan view of hand path. Right column: tangential speed vs. time Top row: early in recovery; piecewise movement, stops and startsBottom row: late in recovery; smoother movement, less speed fluctuation
y-d
ire
cti
on
(m
)
sp
ee
d (
m/s
)
Week 6
-0.2-0.1 0 0.1 0.2-0.2
0
0.2
0 2 4 6 8
Week 6
0
0.2
0.4
-0.2-0.1 0 0.1 0.2-0.2
0
0.2
x-direction (m)
Week 11
0
0.2
0.4
0 2 4 6 8
Week 11
time (sec)
Initial and final position P2
P3
P1
P4P2
XY
MS1=16
MS1=16
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Experiment of Nature
y-d
ire
ctio
n (m
)
x-direction (m)
-0.1 -0.05 0 0.05 0.1 0.15
-0.1
-0.05
0
0.05
0.1
0.15
Subject A,Right Arm-point-to-pointmovement
time (sec)
spe
ed
(m/s
)
0 10 20 300
0.05
0.1
0.15
0.2
0.25
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Beta Density Function
Independent Variable-0.3 -0.1 0.1 0.3 0.5 0.7 0.9 1.1 1.3
No
rma
lize
d P
rob
ab
ility
Den
sity
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
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Motor “Quanta” areHighly Stereotyped
MinimumJerk
Gaussian
- - - - EnsembleBeta
Scattered Beta
-0.3 -0.1 0.1 0.3 0.5 0.7 0.9 1.1 1.30
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Independent Variable
Nor
ma
lize
d P
rob
abili
ty D
ens
ity
subject movement
0 10 20 30 40 5000.20.40.60.8
mean = 0.47 + 0. 04
p
0 10 20 30 40 5000.20.40.60.8
subject movement
mean = 0.18 + 0. 02
ps
Sub-movements extracted from the first two movements by 19 stroke patients. Inserts: mean (p) and standard deviation (ps) of best-fit function for each submovement. (38 individual functions, ensemble best-fit , Gaussian & minimum-jerk curves)
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Good Fit
024681012141618
Eigenvector Direction
Num
ber
of C
urve
s
0 0.20.40.60.8 1 1.21.41.61.8 2
mean = 1.03 + 0.14
duration (sec)0.1 0.3 0.5 0.7 0.9
0
0.2
0.4
0.6
0.8
Nor
mal
ized
F
unct
ion
dashed-ensemble
solid -individual
0 0.10.20.30.40.50.60.70.80.9 10
10
20
30
40
50
Correlation CoefficientN
umbe
r of
Cur
ves mean = 0.91 + 0.14
Assessment of the ensemble best-fit Beta function. The left plot shows an example of an individual speed profile (solid line) compared to the ensemble best-fit Beta function (dashed line). The center plot shows the histogram of the slope of the principal eigenvector of the covariance matrix between the individual speed profiles and the ensemble best-fit Beta function. The histogram on the right shows the correlation coefficient between the individual
speed profiles and the ensemble best-fit Beta function.
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Range of Speeds
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
30
Speed Peak (m/s)
Num
ber
of P
eaks
Stroke Patientsspeed profiles (m/s)(mean = 0.08 + 0.05)
Myoclonus Patientspeed profiles (m/s)(mean = 0.46 + 0.17)
Histogram of the individual speed profiles for stroke and myoclonus patients. The stroke patients’movements were typically slow (left distribution), while the myoclonus patient's involuntary shock-like movements were fast (right distribution), near the maximum capacity of the neuro-muscular system.
Drawing Circles
y-d
ire
ctio
n (
m)
Week 6
Week 7
Week 9
-0.2-0.1 0 0.1 0.2
-0.2-0.1 0 0.1 0.2
-0.2-0.1 0 0.1 0.2
-0.2-0.1 0 0.1 0.2
-0.2
0
0.2
-0.2
0
0.2
-0.2
0
0.2
-0.2
0
0.2
x-direction (m)
Week 11
spee
d (
m/s
)
0 2 4 6 8
Week 6
0 2 4 6 8
Week 7
0 2 4 6 8
Week 9
0
0.2
0.4
0
0.2
0.4
0
0.2
0.4
0
0.2
0.4
0 2 4 6 8
Week 11
time (sec)
Initial and final position P2
P3
P1
P4P2
X
Y
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31 Patients (inpatients & outpatients)
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Rohrer, B., Fasoli, S., Krebs, H.I., Hughes, R., Volpe, B.T., Frontera, W., Stein, J. and Hogan, N., "Movement smoothness changes during stroke recovery," Journal of Neuroscience, 22:18:8297‐8304, 2002.
Smoothness
improves with
recovery Inpatients
generally more
than outpatients
Jerk gets
worse before it
gets better
Smoothness Changes
Submovement merging explains smoothness changes
a) Comparison of smoothness metrics during simulated submovement blending
Δt between onset of submovements (s)
(Var
ious
sca
ling
and
offs
ets)
Sm
ooth
ness
0.2 0.3 0.4 0.5 0.6 0.70
0.2
0.4
0.6
0.8
1
ψ jerkψ peaksψ MAPR
ψ speedψ tent
j p m v t
0
0.1
spee
d (m
/s)
First day of therapy
0
0.1
Last day of therapy
Typical speed profiles
0
0.1
First day of therapy
0 1 2 30
0.1
time (s)
Last day of therapy
Inpa
tient
Out
patie
nt
b)c)
d)
e)
f)
g)
h)
k)
Blending
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Recovery Changes Submovement Features During recovery sub‐movements became
Fewer
Faster
Longer
Overlap of sub‐movements increased
To some extent in every patient
Significantly for 22 of 31 patients
Inter‐peak interval decreased for in‐patients
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Generalization 47 sub‐acute stroke, 19.1 (±1.2) days earlier, 57% males, 61.3 (±1.8 SEM) y.o., 77% right hemisphere lesion
117 chronic stroke, 1150 (±90) days earlier, 63% males, 58.8 (±1.2 SEM) y.o., 54.7% right hemisphere lesion
Dipietro L, Krebs HI, Volpe BT, Stein J, Bever C, Mernoff ST, Fasoli SE, Hogan N, “Learning, Not Adaptation, Characterizes Stroke Motor Recovery: Evidence from Kinematic Changes Induced by Robot‐Assisted Therapy in Trained and Untrained Task in the Same Workspace,” IEEE Trans Neural Sys and Rehab Eng 20:1:48‐57(2012).
Robot and Clinical Metric Sub‐acute: 10.02 (±1.14 SEM) to 22.70 (±2.28) in the FMA
Chronic: 20.47 (±1.15) to 24.35 (±1.27) in the FMA
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Robot Assay trainedreaching movements
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Robot Assay untrained circle drawing
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Correlation Trained and Untrained
Variable
Correlation (trained vs untrained task)
Mean speed (m/s) Peak speed (m/s) Duration (s) Speed shape Number of peaks Jerk (1/s2) Submovement number Submovement peak (m/s) Submovement duration (s) Submovement overlap (s) Submovement interpeak dist (s) Submovement Mu (Skewness) Submovement Sigma (Kurtosis)
0.90* (0.01) 0.83* (0.04) 0.72 (0.10) 0.93* (0.007) 0.85* (0.03) 0.75 (0.08) 0.93* (0.005) 0.88* (0.02) 0.85* (0.03) 0.83* (0.04) 0.56 (0.24) -0.22 (0.67) 0.92* (0.008)
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Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Speed‐Accuracy
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Speed‐Accuracy: Speed Control
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Motor Control Model Macroscopic Level
Speed‐Accuracy
Mesoscopic Level Smoothness Minimum‐Jerk
Microscopic Level Reaction Time Dynamic Movement Primitives
Submovements (discrete) Oscillations (rhythmic) Mechanical Impedances
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Mechanical Impedance (Z)
Neuro-muscular mechanical impedance is
the key to controllingphysical interaction
Hogan and Buerger (Robotics and Automation Handbook,2005)
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Variable Interactive Dynamics 10 unimpaired subjects, level treadmill walking Anklebot applied simultaneous 2D stochastic
perturbation Dorsi‐plantar flexion (DP), inversion‐eversion (IE) Admittance impulse response function (IRF)
estimated at 40 ms intervals
Fit to 2nd order (mass‐spring‐damper) model
Lee H, Krebs HI, Hogan N. “Multivariable Dynamic Ankle Mechanical Impedance with Active Muscles,” IEEE‐ TNSRE 22:5: 971 ‐ 981 (2014).
Lee H, Krebs HI, Hogan N. “Multivariable Dynamic Ankle Mechanical Impedance with Relaxed Muscles,” ,” IEEE‐ TNSRE22:6: 1104 ‐ 1114 (2014).
A trajectory of interactive dynamics
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Time‐Varying Mechanical Impedance: Sagittal Plane (DP)
: Frontal Plane (IE)
PSW ISW MSW TSWEST
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Lee H, Rouse E, Krebs HISummary of Human Ankle Mechanical Impedance during Walking, IEEE Journal of Translational Engineering in Health and Medicine (in press).
Time‐Varying Mechanical Impedanceduring Walking
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Lee H, Rouse E, Krebs HISummary of Human Ankle Mechanical Impedance during Walking, IEEE Journal of Translational Engineering in Health and Medicine (in press).
Time‐Varying Mechanical Impedanceduring Walking
Should We Train Impedance? Relaxed ankle stiffness measured in
4 “cardinal” directions Dorsi‐plantarflexion (DP)
Inversion‐eversion (IE)
3 subject groups Stroke patients (ST) Age‐matched unimpaired (AC) Young unimpaired (YH)
Stroke patient stiffness was higher in 3 out of 4 directions
Roy A, Krebs HI, Bever CT, Forrester LW, Macko RF, Hogan N, “Measurement of Passive Ankle Stiffness in Subjects with Chronic Hemiparesis Using a Novel Ankle Robot,” J.Neurophys; 105:2132‐2149 (2011).
Roy A, Forrester L W, Macko R F, Krebs HI, “Changes in Passive Ankle Stiffness and its Effects on Gait Function in Chronic Stroke Survivors,” VA J Rehab Res Dev, 50:4:555‐572 (2013).
Muscle Activity Onset Time RF ST TA SOL
163 ± 22 ms 129 ± 68 ms 193 ± 80 ms 207 ± 74 ms
Supra‐Spinal Involvement
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Distinct robotic training protocols differentially alter motor recovery in chronic stroke
H. I. Krebs1,2, B. T. Volpe3, C. Bever2,4 , J. Stein5,6,7, S. E. Fasoli8, W. R. Frontera9 , L. Dipietro1 , N. Hogan1,10
1 Massachusetts Institute of Technology, Department of Mechanical Engineering 2 University of Maryland, School of Medicine, Department of Neurology 3 The Feinstein Institute for Medical Research 4 Baltimore Veterans Administration Medical Center 5 Columbia University College of Physicians and Surgeons, Department of Rehabilitation
Medicine 6 Weill Medical College of Cornell University, Division of Rehabilitation Medicine 7 New York Presbyterian Hospital 8 Providence VA Medical Center 9 Vanderbilt University, School of Medicine, Department of Physical Medicine and
Rehabilitation 10 Massachusetts Institute of Technology, Brain and Cognitive Sciences
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Virtual Trajectory Submovements
Oscillations
Impedances
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IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
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Hypotheses
Discrete (submovements): aiming
Rhythmic (oscillations): timing
Impedance: strength
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***
***
***
RhythmicTraining
1994 – Sensorimotor“hand-over-hand”
xbkxF xc ,
ybyykF jmyc ..,
543
.. 161510mmm
mjm t
t
t
t
t
tly
where K is 200 N/m, B is 10 Ns/m, is 0.14m, T is 3 sec, and s isthe hand displacement in one of the 8 directions and s0 is theminimum-jerk reference trajectory.
Discrete Reaching Training
xbkxF xc ,
m
mjm
jm
m
jmbw
yc
ly
lyy
yy
yblyk
ybyyk
F ..
....
, 0
2003 – Performance-Based Algorithm
Krebs et al,“Rehabilitation Robotics: Performance‐based Progressive Robot‐Assisted Therapy, Autonomous Robots, 15:7‐20 (2003).
Adaptive Controller
Implements a “virtual slot” between start and target
Back wall closes in on target
Allows free movement to target
Assists desired movement as needed
Springy walls provide graded assistance
Deter inappropriate movement (aiming)
Tracks patient’s performance
Challenges the patient
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***
***
*
ImpedanceModulation
1999 – ProgressiveStrength Training
xbkxF xc ,
ybkyF yc ,
There were four graded stiffnesses values: 100, 133, 166, and 200N/m, while b is always 10Ns/m. This corresponds to target peak forces of approximately 14, 18.5, 23, and 28N.
Patients were requested to attempt to move their arm against the highest graded stiffness value (200 N/m). The mean of the maximumdistance from the center to the 8 targets was calculated. Patients whose mean was under 3.5cm were challenged in the following
session by the weakest spring value. Patients whose means fell under 7, 10, or 14cm were challenged by the other graded resistances.
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Examples
-0.15 -0.1 -0.05 0 0.05 0.1 0.15-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Displacement x (m)
Dis
pla
cem
ent
y (
m)
b
k
desired end-point
arm stiffness
robot-arm stiffness -0.15 -0.1 -0.05 0 0.05 0.1 0.15-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Displacement x (m)
Dis
plac
emen
t y
(m)
Discrete(Submovements)
Rhythmic(Oscillations)
Impedances
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-0.15 -0.1 -0.05 0 0.05 0.1 0.15-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Displacement x (m)
Dis
pla
cem
en
t y
(m)
Mean (sem) Admission Discharge Follow-Up Repeated ANOVA
N# of subjects 111 111 98 p<0.05 for significance
UE Fugl-Meyer
F-M(Max=66)
20.47
(1.16)
24.35
(1.27)
24.57
(1.40)
F=45.6; p < 0.0001*
Modified
Ashworth
(Max = 56)
10.93
(0.53)
10.15
(0.55)
9.04
(0.65)
F=7.3 p = 0.01*
Clinical Measures at Admission, Discharge and Follow‐Up
Barrow Sep 2016
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28
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p=0.97
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p=0.97
111 severe to moderate chronic outpatients
18 hours of robot therapy
Fugl-Meyer Assessment
VA R&DB2436T
NICHD/NCMRRHD-37397
NICHD/NCMRRHD-36827
Barrow Sep 2016
Clinical Outcomes
Robot Measures at Admission, Discharge and Follow‐Up
Barrow Sep 2016
Sites Combines Percent of Change
Changes from Admission to Discharge (*) statistical significant change p < 0.05.
N# of subjects = 111 Mean (sem) %
Aim (radians) -.117 (0.017) 11%, *
Duration (sec) -1.882 (0.247) 37%, *
Z Force (N) 4.786 (1.122) 22%, *
Ellipse Ratio 0.111 (0.013) 21%, *
Number of Submovements -5.3 (0.7) 37%, *
Duration Submovements (millisec) 101 (12) 14%, *
Overlap of Submovements (millisec) 34 (4) 14%, *
Amplitude of Submovements (cm/s) 0.9 (0.2) 21%, *
Inter-Peak Distance of Subm (millisec) -11 (12) -2.7%
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
Barrow Sep 2016
Hypotheses
Discrete (submovements): aiming
Rhythmic (oscillations): timing
Impedance: strength
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29
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
R = R1+R2
(Strength)
S = S1+S2
(Rhythmic
Reaching
Sensorimotor)
PB = P + IntP
(Discrete
Reaching
Performance)
ANOVA Fisher’s
PLSD Post-Hoc
Changes
Admission to
Discharge
N# of
Subjects
15 14 23 p–value 0.05
signif.
Aim (radians) -0.048
(0.045)
-0.027
(0.054)
-0.152
(0.041)
R vs S: p=0.74
R vs P: p=0.10
S vs P: p=0.04*
Duration (sec) -1.836
(0.409)
-3.542
(1.189)
-1.031
(0.484)
R vs S: p=0.08
R vs P: p=0.49
S vs P: p=0.01*
Z Force (N) 11.666
(3.536)
3.959
(3.826)
0.087
(2.207)
R vs S: p=0.08
R vs P: p=0.005*
S vs P: p=0.36
Ellipse Ratio 0.027
(0.023)
0.147
(0.033)
0.117
(0.036)
R vs S: p=0.03*
R vs P: p=0.06
S vs P: p=0.54
Reaching movements made by
patients with corpus striatum
lesion – CS (8.9 cm3) and
corpus striatum plus cortex --
CS+ (109.9 cm3) lesions.
The left column shows a plan
view of the patients’ hand path
attempting a point-to-point
movement. The right column
shows hand speed.
0 10 20 30time (sec)
-0.1
-0.05
0
0.05
0.1
0.15
-0.1 -0.05 0 0.05 0.1 0.15x-direction (m)
Subject A,Right Arm-point-to-pointmovement
CS+
Subject P,Right Arm-point-to-pointmovement
-0.1
-0.05
0
0.05
0.1
0.15
-0.1 -0.05 0 0.05 0.1 0.15x-direction (m)
0
0.05
0.1
0.15
0.2
0.25
0
0.05
0.1
0.15
0.2
0.25
spee
d (m
/s)
spee
d (m
/s)
0 10 20 30time (sec)
CS
y-di
rect
ion
(m)
y-di
rect
ion
(m)
Backdriveable Low Impedance Robot‐Based Measurements
Barrow Sep 2016
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
IntensivePerformance-Based1.50 (0.92)
PerformanceBased 7.28 (1.29)
ProgressiveResistance4.53 (0.81)
Interactive Sensorimotor4.48 (0.91)
p<0.
05
p<0.05 p<0.05
p=0.97
p>0.
05 p>0.05
Barrow Sep 2016
Basis for Modeling
Discrete (submovements)
Rhythmic (oscillations)
Impedance
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30
New MechatronicsDesigns for Lower Extremity Rehab
Anklebot
MIT‐Skywalker
Barrow Sep 2016
Performance‐Based Adaptive Control Algorithm
• Macroscopic Level: Speed‐Accuracy Tradeoff
• Mesoscopic Level: Speed Profile
• Microscopic Level: Reaction Time
Tracks Performance – and ‐ Challenges PatientKrebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe BT, Hogan N, “Rehabilitation Robotics: Performance‐based Progressive Robot‐Assisted Therapy,” Autonomous Robots, Kluwer Academics 15:7‐20, 2003.
Anklebot
Barrow Sep 2016
9/7/2016
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Performance
Splat distance
Gate width
Speed(rate of falling gates)
Performance
Speed(of the ball)
Paddle width
Speed of defending the goal
Performance
Speed(of the ball)
Paddle width
9/7/2016
32
Feedback
Barrow Sep 2016
Performance Metric Gameplay / Controller parameter
Barrow Sep 2016
Michmizos K, Rossi S, Castelli E, Cappa P, Krebs HI. "Robot‐Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation." IEEE‐ TNSRE 23:6: 1056‐1067 (2015).
9/7/2016
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MIT Skywalker: A Novel Robot for Gait Rehabilitation of Stroke and Cerebral
Palsy Patients
TRIZDesign & Development Invention
Barrow Sep 2016
Locomotion is a dynamicprocess
Governed by interaction between neural and mechanical processes
Much of locomotorkinematic coordination apparently arises from (passive) mechanics
Passive walker based on the idea of Tad McGeer who pioneered the field. The robot could walk down a plank without power, sensors, or a control system.
Passive Walkers
Barrow Sep 2016
9/7/2016
34
MIT‐Skywalker: Main Concept
heel-strike mid-stance toe-off swing heel-strike
stance phase swing phase
heel-strike mid-stance toe-off swing heel-strike
stance phase swing phase
MIT‐Skywalker Prototype MIT‐Skywalker 1
MIT‐Skywalker 2
• Passive leg forward (swing) movement
• Doesn’t impose rigid kinematics patterns of gait
• Maximize the amount of sensory inputs to neural circuits
• Main Features
Barrow Sep 2016
Barrow Sep 2016
Basis for Modeling
Discrete (submovements)
Rhythmic (oscillations)
Impedance
9/7/2016
35
Videos Skywalker
Barrow Sep 2016
Walking Performance‐Bowden M et al: Physical Therapy Adjuncts to Promote
Optimization of Walking Recovery After Stroke, Stroke Research and Treatment 2011
Motor Control
CV Fitness
Dynamic Balance Control
Strength
Barrow Sep 2016
Walking Performance
Discrete
Movements
CV Fitness
Impedance & Balance
Rhythmic Movements
Barrow Sep 2016
9/7/2016
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Remarks Locomotion is a complex dynamic process
Effective therapy may require “breaking it down” into primitive elements Balance (Mechanical Impedance)
Point attractor?
Mechanical impedance attractor?
Interaction (Mechanical Impedance) Mechanical impedance attractor?
Stepping (Discrete Movements / Submovements) Discrete trajectory attractor?
Rhythm (Rhythmic Movements / Oscillations) Limit‐cycle oscillatory attractor?
Barrow Sep 2016
Hermano Igo Krebs
Barrow Sep 2016
MassachusettsInstitute ofTechnology