Paradigm Shift in Neuroscience - Barrow Neurological Institute€¦ · 65 15 10 T t s t Wrist Speed...

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Hermano Igo Krebs

IEEE Fellow

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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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Speed‐Accuracy




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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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Speed‐Accuracy




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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).


Speed‐Accuracy




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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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Speed‐Accuracy




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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

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-0.2

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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

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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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Speed‐Accuracy




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Speed‐Accuracy

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Speed‐Accuracy: Speed Control

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Speed‐Accuracy




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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




Interactive Sensorimotor4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





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

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4.48 (0.91)

p<0.

05

p=0.97





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

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Clinical Outcomes

Robot Measures at Admission, Discharge and Follow‐Up

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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%




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





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




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





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




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05




4.48 (0.91)

p<0.

05

p<0.05 p<0.05

p=0.97

p>0.

05 p>0.05





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

9/7/2016

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

31

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

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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

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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

[email protected]

Barrow Sep 2016

MassachusettsInstitute ofTechnology

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