dutta anirban phd thesis

8/3/2019 Dutta Anirban PhD Thesis

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DEVELOPMENT OF AN ELECTROMYOGRAM-BASED CONTROLLER FOR

FUNCTIONAL ELECTRICAL STIMULATION-ASSISTED WALKING AFTER

PARTIAL PARALYSIS

By

ANIRBAN DUTTA

Dissertation Advisor: Dr. Ronald J. Triolo

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE CASE WESTERN RESERVE UNIVERITY IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Department of Biomedical Engineering

CASE WESTERN RESERVE UNIVERSITY

May, 2009


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CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

_____________________________________________________

candidate for the ______________________degree *.

(signed)_______________________________________________(chair of the committee)

________________________________________________

________________________________________________

________________________________________________

________________________________________________

________________________________________________

(date) _______________________

*We also certify that written approval has been obtained for any

proprietary material contained therein.

Anirban Dutta

Ph.D.

Dr. Robert F. Kirsch

Dr. Ronald J. Triolo

Dr. Patrick E. Crago

Dr. Roger D. Quinn

03/18/2009


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Sarasvathi Namastubhyam, Varade Kaamaroopini

Vidyaarambham Karishyaami, Siddhir Bhavatu Mey Sada


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TABLE OF CONTENTS

page

LIST OF TABLES...........................................................................................................................4

LIST OF FIGURES .........................................................................................................................6

Abstract..........................................................................................................................................13

Preface............................................................................................................................................15

Acknowledgements........................................................................................................................16

Introduction....................................................................................................................................18

Functional Electrical Stimulation (FES) for ambulation........................................................18

The hardware for the FES-controller......................................................................................19Electromyogram as a command source for FES-controller for ambulation after

iSCI .....................................................................................................................................21

Electromyogram-based trigger for the FES-controller: specific objectives ofthe work .......................................................................................................................23

Overview of the chapters........................................................................................................24

References...............................................................................................................................25Figures ....................................................................................................................................28

Evaluation of surface electromyogram from partially paralyzed muscles as acommand source for functional electrical stimulation............................................................33

Abstract...................................................................................................................................33

Introduction.............................................................................................................................34

Methods ..................................................................................................................................35Subjects............................................................................................................................35

Test of Controllability .....................................................................................................36

Test of Discriminability...................................................................................................38Statistical Analysis ..........................................................................................................42

Results.....................................................................................................................................43

Results from the Test of Controllability..........................................................................43Results from the Test of Discriminability .......................................................................44

Discussion...............................................................................................................................46

Conclusion ..............................................................................................................................48References...............................................................................................................................49

Figures ....................................................................................................................................51

Tables......................................................................................................................................60

Feasibility analysis of surface EMG-triggered FES-assisted ambulation after

incomplete spinal cord injury .................................................................................................66

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Abstract...................................................................................................................................66Introduction.............................................................................................................................66

Methods ..................................................................................................................................68

Subjects............................................................................................................................68Data Acquisition and Processing.....................................................................................70

Muscle Selection .............................................................................................................72Classifier Development and Offline Testing...................................................................73Classifier Testing During FES-assisted Ambulation.......................................................75

Results.....................................................................................................................................76

Classifier Performance ....................................................................................................76

Repeatability of the Classifier Performance....................................................................77Discussion...............................................................................................................................77

Conclusion ..............................................................................................................................79

References...............................................................................................................................80Figures ....................................................................................................................................84

Surface EMG-triggered FES-assisted gait parameters during over-ground walking in

the laboratory ..........................................................................................................................91

Abstract...................................................................................................................................91

Introduction.............................................................................................................................92

Methods ..................................................................................................................................93Subjects............................................................................................................................93

Gait Data Acquisition ......................................................................................................94

Gait Parameters ...............................................................................................................97Statistical Analysis ..........................................................................................................98

Results.....................................................................................................................................98

Discussion...............................................................................................................................99

Conclusion ............................................................................................................................102References.............................................................................................................................103

Figures ..................................................................................................................................104Tables....................................................................................................................................109

Coordination and stability of surface EMG-triggered FES-assisted overground

walking in the laboratory ......................................................................................................111

Abstract.................................................................................................................................111

Introduction...........................................................................................................................111

Methods ................................................................................................................................114

Subjects..........................................................................................................................114Gait Data Acquisition ....................................................................................................115

Coordination and Stability Analysis of Gait initiation..................................................118Results...................................................................................................................................122

Linear regression model for gait initiation ....................................................................122

Coordination and stability during FES-assisted gait initiation......................................124Discussion.............................................................................................................................125

Conclusions...........................................................................................................................129

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References.............................................................................................................................130Figures ..................................................................................................................................133

Development of an implanted intramuscular EMG-triggered FES system for

ambulation after incomplete spinal cord injury ....................................................................143

Abstract.................................................................................................................................143

Introduction...........................................................................................................................144

Methods ................................................................................................................................146Subjects..........................................................................................................................146

Command source selection............................................................................................147

Implantation of intramuscular EMG electrode..............................................................150Classifier development for iEMG-triggered FES-assisted stepping .............................151

Online testing of the classifier in the laboratory ...........................................................155

Results...................................................................................................................................157

Muscles and location selection for intramuscular EMG ...............................................157Classifier development and online performance...........................................................158

Discussion.............................................................................................................................163

Conclusions...........................................................................................................................166References.............................................................................................................................167

Figures ..................................................................................................................................171

Tables....................................................................................................................................185

CURRENT CHALLENGES AND RECOMMENDATIONS FOR Future work .......................188

Introduction...........................................................................................................................188

Evaluating the information content in EMG ........................................................................190Optimal number of features in the EMG.......................................................................191

Optimal number of muscles or channels of EMG .........................................................194Computational requirements for the external controller.......................................................197Surface EMG gait data collection for selecting iEMG command sources...........................198

Optimizing and verifying the iEMG electrode location prior to surgery .............................199Summary...............................................................................................................................200

References.............................................................................................................................201

Figures ..................................................................................................................................203

Appendix......................................................................................................................................213

Bibliography ................................................................................................................................250

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LIST OF TABLES

Table page

Table 2.1: The mean, the minimum, and the maximum average absolute tracking

error in %MVC during the four parts (0-25 sec, 25-50 sec, 50-75 sec, 75-100sec) of the Test for Controllability. The p-value from the one-way two-tailedANOVA test for the average tracking error over the whole trial (100 sec) was

not statistically significant ( 0.01).................................................................................60

Table 2.2: The results from the Test of Discriminability for the muscles Gluteus

Medius (GM), Biceps Femoris (BF), Medial Gastrocnemius (MG), RectusFemoris (RF), Tibialis Anterior (TA), and Erector Spinae (ES at T9) are

presented for the able-bodied subjects. The Wilcoxon statistic (W) was

similar in magnitude to the corresponding Discriminability Index (DI).

Similarly the Standard Deviation (SD) of the DI over 10 random partitions(i.e., 10-fold cross-validation) was similar in magnitude to the Standard Error

(SE) found for the Wilcoxon statistic (W). There were statistically

significant (p 0.05) differences in the means of DI due to the muscle typeas well as the classifier type...............................................................................................61

Table 2.3a: The results from the Test of Discriminability of iSCI-1 for the left step

classifier. The Wilcoxon statistic (W) was similar in magnitude to the

corresponding value of the Discriminability Index (DI). Similarly the

Standard Deviation (SD) of the DI was similar in magnitude to the StandardError (SE) found for the Wilcoxon statistic (W). There were statistically

significant (p 0.05) differences in the means of DI due to the muscle type

as well as the classifier type...............................................................................................62

Table 2.3b: The results from the Test of Discriminability of iSCI-1 for the right step.The Wilcoxon statistic (W) was similar in magnitude to the corresponding

value of the Discriminability Index (DI). Similarly the Standard Deviation(SD) of the DI was similar in magnitude to the Standard Error (SE) found for

the Wilcoxon statistic (W). There were statistically significant (p 0.05)

differences in the means of DI due to the muscle type as well as the classifiertype.....................................................................................................................................63

Table 2.4a: The results from the Test of Discriminability of iSCI-2 for the left step.The Wilcoxon statistic (W) and the corresponding value of the

Discriminability Index (DI) were similar. The Standard Deviation (SD) of theDI and the Standard Error (SE) found for the Wilcoxon statistic (W) weresimilar. There were statistically significant (p 0.05) differences in themeans of DI due to the muscle type as well as the classifier type. ....................................64

Table 2.4 b: The results from the Test of Discriminability of iSCI-2 for the right step

classifier. The Wilcoxon statistic (W) and the corresponding value of the

Discriminability Index (DI) were similar. The Standard Deviation (SD) of the

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DI and the Standard Error (SE) found for the Wilcoxon statistic (W) weresimilar. There were statistically significant (p 0.05) differences in themeans of DI due to the muscle type as well as the classifier type. ....................................65

Table 4.1: The Mean, Standard Deviation (S.D), coefficient of variation (C.V.), 95%

confidence interval (95% C.I.) over 10 trials (N=10) of the EMG-triggeredand switch-triggered gait parameters gait speed (m/s), left step length (m),right step length (m), left double support duration (s), right double support

duration (s), left swing phase duration (s), right swing phase duration (s) for

the subject iSCI-1. [ statistically significant (p


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LIST OF FIGURES

Figure page

Figure 1.1: Components of the FES system. .................................................................................28

Figure 1.2: Universal External Control Unit (UECU) with inductive coil and finger

switch. ................................................................................................................................29

Figure 1.4: Universal External Control Unit (UECU) stand-alone configuration

[1.22]..................................................................................................................................31

Figure 1.5: Universal External Control Unit (UECU) configuration with xPC target

PC [1.22]............................................................................................................................32

Figure 2.1: Experimental setup for the Test of Controllability of the surface EMGfrom Rectus Femoris using visual pursuit tasks while the knee was fixed in a

dynamometer......................................................................................................................51

Figure 2.2: Experimental setup for surface EMG data collection with switch-

triggered FES-assisted overground walking. .....................................................................52

Figure 2.3: Experimental protocol for surface EMG data collection during

overground walking, where the subject had to start from standing and achievea self-selected gait speed within 5m. .................................................................................53

Figure 2.4: The left column shows the cumulative distribution function for the three

cases, 1,15.0,5.00 DIDIDI and the right column shows the

corresponding Receiver Operating Characteristics curve..................................................54

Figure 2.5: TRACKING (broken black line) and TARGET (solid black line) signalsduring visual pursuit task for the Test of Controllability. The boxes at each

data point show the lower quartile and upper quartile values of the

TRACKING signal. Whiskers extending at the top and bottom of the boxes

show the range of the TRACKING signal. The top panel presents the resultsfor iSCI-1 and the bottom panel for iSCI-2. The left panel presents the results

for the left Rectus Femoris and the right panel presents the results for the

right Rectus Femoris..........................................................................................................55

Figure 2.6: TRACKING (broken black line) and TARGET (solid black line) signals

during visual pursuit task for the Test of Controllability with able-bodiedsubjects. The boxes at each data point show the lower quartile and upperquartile values of the TRACKING signal. Whiskers extending at the top and

bottom of the boxes show the range of the TRACKING signal........................................56

Figure 2.7: Top panel shows the results from the post hoc analysis of the

Discriminability Index with their critical values from Scheffes S procedure

for the muscles Gluteus Medius (GM), Biceps Femoris (BF), Medial

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Gastrocnemius (MG), Rectus Femoris (RF), Tibialis Anterior (TA), andErector Spinae (ES at T9) obtained from the Test of Discriminability with

able-bodied subjects. The bottom panel shows the results from the post hoc

analysis of the Discriminability Index with their critical values fromScheffes S procedure for different classifiers Pattern Recognition

Classifier (PRC) and Threshold-based Classifier (TC) obtained from the Testof Discriminability with able-bodied subjects...................................................................57


Discriminability Index with their critical values from Scheffes S procedurefor the muscles Gluteus Medius (GM), Biceps Femoris (BF), Medial

Gastrocnemius (MG), Rectus Femoris (RF), Tibialis Anterior (TA), and

Erector Spinae (ES at T9) obtained from the Test of Discriminability of theleft and right step classifiers of iSCI-1. The bottom panel shows the results

from the post hoc analysis of the Discriminability Index with their critical

values from Scheffes S procedure for different classifiers PatternRecognition Classifier (PRC) and Threshold-based Classifier (TC) obtained

from the Test of Discriminability of the left and the right step classifiers of

iSCI-1.................................................................................................................................58


Discriminability Index with their critical values from Scheffes S procedurefor the muscles Gluteus Medius (GM), Biceps Femoris (BF), Medial

Gastrocnemius (MG), Rectus Femoris (RF), Tibialis Anterior (TA), and

Erector Spinae (ES at T9) obtained from the Test of Discriminability of theleft and right step classifiers of iSCI-2. The bottom panel shows the results

from the post hoc analysis of the Discriminability Index with their critical

values from Scheffes S procedure for different classifiers Pattern

Recognition Classifier (PRC) and Threshold-based Classifier (TC) obtainedfrom the Test of Discriminability of the left and the right step classifiers of

iSCI-2.................................................................................................................................59

Figure 3.1: a) X-ray of the iSCI subject implanted with implantable receiver-stimulator (IRS-8) b) iSCI subject stepping with the switch-triggered FES

system. ...............................................................................................................................84

Figure 3.2: Experimental setup for testing EMG-triggered FES-assisted walking with

the block-diagram for the EMG-triggered FES system (ECU: external control

unit, LE: linear envelope). .................................................................................................85

Figure 3.3: Processing of the sampled EMG from Erector Spinae for training theclassifier a) rectified and reconstructed EMG signal b) linear envelope found

from processed EMG signal...............................................................................................86

Figure 3.4: Muscle selection for the classifier using receiver operating characteristics

curve from switch-triggered FES-assisted gait data (FS: Foot-Strike, FO:

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Foot-Off) a) linear envelope (LE) indicating class True b) linear envelope(LE) indicating class False. .............................................................................................87

Figure 3.5: Receiver operating characteristics curve of the classifiers using the test

data.....................................................................................................................................88

Figure 3.6: State transition diagram of the EMG-based FES-controller. ......................................89

Figure 3.7: Offline testing of the classifier using receiver operating characteristics

curve a) time-error (negative means prediction) in detection of foot-off by the

classifier b) duration of the gait phases (left DS: double support phasefollowing left swing phase, right DS: double support phase following right

swing phase, SW: swing phase).........................................................................................90

Figure 4.1: Experimental setup for testing EMG-triggered FES-assisted walking with

the block-diagram for the EMG-triggered FES system (ECU: external control

unit)..................................................................................................................................104

Figure 4.2: EMG-based gait event detector for triggering FES-assisted steps...........................105

Figure 4.3: Plot of the Root Mean Square Error (RMSE) between the low-pass

filtered and unfiltered foot progression in sagittal plane with cut-offfrequencies to find the optimum cut-off frequency for low-pass filtering the

gait kinematics data. Optimum cut-off frequency was found to be 3.5 Hz for

iSCI data...........................................................................................................................106

Figure 4.4: Gait data collection protocol in laboratory conditions where the subject

had to start from standing and achieve a self-selected gait speed within

motion analysis systems volume of data capture (~5m).................................................107

Figure 4.5: Boxplot of average body weight support provided by the walker during

EMG-triggered (N=10 trials) and switch-triggered (N=10 trials) gait

normalized by the mean of that during EMG-triggered trials of iSCI-2. Thebox shows the lower quartile, median, and upper quartile with whiskers

extending at each end showing the range of the data. The notches around themedian show the estimate of the uncertainty. The boxes whose notches dont

overlap indicate that their medians differ at 5% significance level.................................108

Figure 5.1: Laboratory setup for EMG-triggered FES-assisted walking shown with a

flowchart for the EMG-based gait event detector for triggering FES-assisted

steps..................................................................................................................................133

Figure 5.2: Top panel: Selection of optimum cut-off frequencies for low-passfiltering the kinematic data. Bottom panel: Most of power content in the

signals was below the optimum cut-off frequency, which were 6 Hz for able-

bodied and 3.5 Hz for iSCI data.......................................................................................134

Figure 5.3: Gait initiation protocol during the data collection.....................................................135

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Figure 5.4: Typical pelvis motion in the direction of progression during gaitinitiation. ..........................................................................................................................136

Figure 5.5: Euclidean distance from the origin of the perturbation of the 36 states

during gait initiation at the maximum left knee flexion. left panel: able-

bodied data (4 subjects). Middle panel: iSCI data (subject C1). right panel:iSCI data (subject C2). [Normative: 4 subjects, 10 trials each; iSCI EMG-trigger: 2 subjects, 10 trials each; iSCI Switch-trigger: 2 subjects, 10 trials

each; iSCI Auto-trigger: 2 subjects, 10 trials each]. ........................................................137

Figure 5.6: Percent Variance Accounted For (%VAF) by the Principal Components

(PC). Top panel: able-bodied data. Middle panel: iSCI-1 walking with EMG,switch, and auto triggered FES. Bottom panel: iSCI-2 walking with EMG,

switch, and auto triggered FES. All the plots show the data averaged over 6

gait events. .......................................................................................................................138

Figure 5.7: Typical loading of the first 3 Principal Components (PCs) on the joint

angles (HA: Hip Angle, KA: Knee Angle, AA: Ankle Angle) found from theweight matrix of the subject Able1. The prefix l indicates the left side

and r indicates the right side. The suffix x denotes sagittal plane, ydenotes frontal plane, and z denotes transverse plane for the joint angles....................139

W

Figure 5.8: Euclidean distance from the origin of the perturbation of the 5 principalcomponents at maximum left knee flexion left panel: able-bodied (4

subjects). Middle panel: iSCI-1 subject C1. right panel: iSCI-2 subject C2.

[Normative: 4 subjects, 10 trials each; iSCI EMG-trigger: 2 subjects, 10 trialseach; iSCI Switch-trigger: 2 subjects, 10 trials each; iSCI Auto-trigger: 2

subjects, 10 trials each]. ...................................................................................................140

Figure 5.9: Top panel: Scatter plot of QoF and Av. Eig. at 6 gait events for the

groups; the 4 able-bodied subjects: Able1, Able2, Able3, Able4, and the 2

iSCI subjects with different trigger modes: EMG1, EMG2, SW1, SW2,Auto1, Auto2. Bottom panel: MANOVA cluster dendrogram plot of the

groups...............................................................................................................................141

Figure 5.10: Mahalanobis distances matrix between each pair of group means. ........................142

Figure 6.1: Experimental setup for data collection during FES-assisted walking with

the block-diagram for the FES system (ECU: external control unit, LE: linear

envelope)..........................................................................................................................171

Figure 6.2: Processing of the sampled surface EMG a) rectified and reconstructedsEMG signal b) linear envelope found from processed sEMG signal.............................172

Figure 6.3: Experimental protocol for the collection of EMG data during over-ground walking in the laboratory.....................................................................................173

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Figure 6.4: Examples of the multi-electrode matrix for simultaneous collection of thesurface EMG from multiple locations on the muscle belly. ............................................174

Figure 6.5: The steps during the implantation of intramuscular EMG electrode a)

insertion of probe, b) deployment of peelable sheath over probe, c) insertion

of the iEMG electrode through the peelable sheath, d) peeling off of thepolymer sheath leaving the iEMG electrode in place. .....................................................175

Figure 6.6: Pulse-width map of iSCI-1 i.e., the stimulation patterns with time as x-

axis and pulse-width in s as the y-axis that was used for FES-assistedwalking (Implanted muscles LIL: left iliopsoas, LES: left erector spinae,LGM: left gluteus maximus, LQU: left vastus intermedius/lateralis, RIL:

right iliopsoas, RTFL: right tensor fasciae latae, RTA: right tibialis anterior,

RES: right erector spinae, RGM: right gluteus maximus, RQU: right vastusintermedius/lateralis, RHS: right hamstring, RPA: posterior portion of the

right adductor magnus). ...................................................................................................176

Figure 6.7: The real-time cycle in IST-12 with 50 ms time period for stimulationfrequency of 20 Hz...........................................................................................................177

Figure 6.8: Parameters for the iEMG classifier computed from the training data that

was collected with the switch-triggered FES system.......................................................178

Figure 6.9: The flow chart of the iEMG-based two-stage classifier for triggering FES

for walking.......................................................................................................................179

Figure 6.10: Usability Rating Scale to find the user perspective on ease/difficulty ofusing the classifier [6.29].................................................................................................180

Figure 6.11: Best location found from the surface EMG for implanting intramuscularEMG electrodes a) left gastrocnemius and right erector spinae b) left and

right gastrocnemius..........................................................................................................181

Figure 6.12: a) Discriminability Index (DI) of left medial gastrocnemius (MG) for

the swing phase (SW) and double support phase (DS) during over-groundwalking for the subject iSCI-1 at each data point of the gait cycle b)

Discriminability Index (DI) of right erector spinae (ES) for the swing phase(SW) and double support phase (DS) during over-ground walking for the

subject iSCI-1 at each data point of the gait cycle. [Shaded portion is the

classification region used by the classifiers]....................................................................182

Figure 6.13: Inhibition of iEMG from right erector spinae during right swing phase

(SW) as shown in the left panel due to electrical stimulation (shaded portion)of the same muscle when compared to that in absence of electrical

stimulation shown in the right panel of the subject iSCI-1..............................................183

Figure 6.14b: a) Discriminability Index (DI) of right medial gastrocnemius (MG) for

the swing phase (SW) and double support phase (DS) during over-ground

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walking for the subject iSCI-2 at each data point of the gait cycle b)Discriminability Index (DI) of left medial gastrocnemius (MG) for the swing

phase (SW) and double support phase (DS) during over-ground walking for

the subject iSCI-2 at each data point of the gait cycle. [Shaded portion is theclassification region used by the classifiers]....................................................................184

Figure 7.1: An example of the classes True and False clustered in the featurespace defined by only three features found from the linear envelope of left

Erector Spinae (shown in left panel) and right Erector Spinae (shown in right

panel)................................................................................................................................203

Figure 7.2: a) Discriminability Index for the left step classifier with the ROC plotfrom a feature space with 1, 2, 3 or 4 features and based on the surface EMG

from gluteus medius (GM), biceps femoris (BF), medial gastrocnemius

(MG), rectus femoris (RF), tibialis anterior (TA), and erector spinae (ES at

T9). b) Discriminability Index for the right step classifier with the ROC plotfrom a feature space with 1, 2, 3 or 4 features and based on the surface EMG

from gluteus medius (GM), biceps femoris (BF), medial gastrocnemius

(MG), rectus femoris (RF), tibialis anterior (TA), and erector spinae (ES atT9)....................................................................................................................................204

Figure 7.3: a) Discriminability Index for the left step classifier with the ROC plot

from a feature space with 1, 2, 3 or 4 features and based on the surface EMG

from left medial gastrocnemius (MG). b) Discriminability Index for the right

step classifier with the ROC plot from a feature space with 1, 2, 3 or 4features and based on the surface EMG from right medial gastrocnemius

(MG). ...............................................................................................................................205

Figure 7.4: a) Discriminability Index (DI) for the left step classifier versus thenumber of muscles added in the increasing order of their individual DI. b)Discriminability Index (DI) for the right step classifier versus the number of

muscles added in the increasing order of their individual DI. .........................................206

Figure 7.5: a) Surface EMG patterns during gait from able-bodied subjects from

muscles lateral gastrocnemius (GL), medial gastrocnemius (GM), peroneuslongus (PL), biceps femoris (BF), rectus femoris (RF), tibialis anterior (TA),

gluteus medius (GD), vastus lateralis (VL), vastus medialis (VM), and

adductor longus (AD) are clustered in 4 groups based on their crosscorrelation coefficients, shown by the dendogram plot. b) Three principal

components (or synergies Syn1, Syn2, and Syn3) found from those surfaceEMG patterns which accounted for more that 90% variance in the data.........................207

Figure 7.6: For able-bodied subjects, the gait events such as heel strike, contralateral

foot off, mid stance, contralateral heel strike, ipsilateral foot off, maximumknee flexion, mid swing are clustered (green dots) in the feature space

defined by only first 3 principal components (or synergies). ..........................................209

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Figure 7.7: Discriminability Index (DI) for the left (black) and right (red) stepclassifier versus the duration of unblanked surface EMG from left (for left

classifier) and right (for right classifier) medial gastrocnemius muscle..........................210

Figure 7.8: Schematic representation of a PC/104+ single board computer running

xPC target (The Mathworks Inc., USA) interfaced with UECU to supplementits computational resources..............................................................................................211

Figure 7.9: Driven gait orthosis (DGO) like Lokomat shown here to control thepatients leg trajectories in sagittal plane during walking [photo taken from

7.5]. ..................................................................................................................................212

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13

DEVELOPMENT OF AN ELECTROMYOGRAM-BASED CONTROLLER FORFUNCTIONAL ELECTRICAL STIMULATION-ASSISTED WALKING AFTER

PARTIAL PARALYSIS

ABSTRACT

by

ANIRBAN DUTTA

Paralysis can be caused by an injury to the spinal cord that may partially or

completely interrupt communication between the brain and the muscles. If the paralyzed

muscles below the level of injury remain innervated then they can be activated by

applying small electrical currents in a process known as Functional Electrical Stimulation

(FES). The electromyogram (EMG) is the time history of the electrical activity of a

muscle that can be used to find its level of activation. This dissertation investigated the

use of EMG as a command source for FES-assisted ambulation after incomplete spinal

cord injury (iSCI). The synergistic modulation of the volitional EMG was used to identify

the intent to transition from step to step even when partially paralyzed muscles were too

weak to produce enough moment at the joint to produce effective push-off.

This work has shown that:

1. The controllability of the surface EMG from a partially paralyzed muscle from

individuals with iSCI during a visual pursuit task was similar to able-bodied subjects.

2. Surface EMG from the ipsilateral erector spinae and medial gastrconemius

consistently performed well to identify the intent to step in able-bodied and iSCI subjects.

3. Spatio-temporal gait parameters with EMG-triggering were at least as good as

with standard switch-triggered FES for iSCI subjects in spite of the differences in their


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injury levels, degree of preserved volitional control, and muscle set chosen for

stimulation.

4. EMG-triggering improved the coordination of the FES-assisted iSCI gait during

stand-to-walk transitions to levels similar to able-bodied gait.

5. Command sources can be selected objectively prior to implementing a fully

implantable EMG-triggered FES system for walking.

6. The optimal number of command sources, features, and signal processing

techniques can be determined to further improve the accuracy of EMG-triggering.

More research is needed to optimize the implantation site for EMG recording

electrodes and define the technical requirements for a clinically practical EMG-triggered

system to facilitate ambulation after iSCI.

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PREFACE

This dissertation compiles my research work at the Cleveland FES Center during

my doctoral studies in the Department of Biomedical Engineering at the Case Western

Reserve University. The research work was a part of my job as a research assistant at the

Cleveland Louis Stoke Veterans Affairs Medical Center, to which I was affiliated from

spring 2004 to summer 2008.

This document is divided into seven chapters. This research work is a link that

starts based on prior work and ends where it lends itself to future work. The first chapter

starts the link where it gives an introduction to prior work and lays out the organization of

this study. The six chapters after that are written as manuscripts that are either accepted

or intended for publication in peer-reviewed journals. The last chapter ends the link

where it extrapolates the results obtained during the course of this study that can lend

itself to future work.

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ACKNOWLEDGEMENTS

This work is indebted to the help and support obtained from many people. First and

foremost is my research advisor, Dr. Ronald Triolo whose ideas, advice, and inspirations

made this work possible. Then I would like to thank my Ph.D. supervisory committee

consisting of Dr. Robert Kirsch, Dr. Patrick Crago, and Dr. Roger Quinn who provided a

sounding board, sound advice, and encouragement during the course of this study.

My journey in the world of research started with my year-long undergraduate

project in the laboratory of Dr. Bireswar Majumdar. I want to thank him for instilling in

me the love for research and for tuning my mental compass to take a middle path between

theoretical and experimental research.

I am indebted to my friends for providing me with a stimulating environment to

pursue my graduate study. I am especially grateful to Nasir Shaikh, Saangiit Srivastava,

Niraj Bidkar, and Sanjay Solanki at University of Florida; Ashvin Mudaliar at Virginia

Tech; Curtis To, Raviraj Nataraj, Vanessa Everding, J. Luis Lujan, Lee Fisher, Tom

Bulea, and Steve Gartman at Case Western Reserve University.

I am grateful to the Cleveland FES Center for bringing together researchers,

clinicians, and engineers under one roof. I want to thank everyone at the Motion Study

Laboratory, especially Rudi Kobetic, Dr. Elizabeth Hardin, Dr. Musa Audu, Stephanie

Bailey, Lori Rohde, Lisa Boggs, Mike Miller, John Schnellenberger, and Barb Seitz. This

work is indebted to the technical support provided by the Technology Development

Laboratory of the Cleveland FES Center.

I wish to thank my family for providing me with a loving environment. This

includes my extended family and friends, especially my best friend Rachna Kumar

for helping me through difficult times, and for all the emotional support and caring, fights

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and camaraderie. Lastly, and most importantly I thank my parents Durgadas Dutta and

Sunanda Dutta and my brother Arindam Dutta for always loving and supporting me

in whatever I do. This work is dedicated to them and The Almighty.

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CHAPTER 1INTRODUCTION

Functional Electrical Stimulation (FES) for ambulation

Paralysis can be caused by an injury to the spinal cord that may partially or

completely damage the communication between the brain and the muscles. The spinal

cord injury (SCI) can be complete or incomplete based on the extent of damage to the

communication channels between the brain and the lower motor neurons below the level

of injury. There are approximately 250,000 people living with SCI in USA and about

11,000 new cases each year [1.1]. If the paralyzed muscles below the level of injury

remain innervated after the injury then they can be electrically activated by applying a

series of electrical current pulses. Functional Electrical Stimulation (FES) refers to the

application of electrical pulses to restore neuromuscular function after paralysis. FES was

first used by Liberson for actuating paralyzed limbs [1.2]. FES has been successful in

providing walking function to spinal cord injured individuals with limited or no walking

abilities [1.3]. Most of the commercially available FES systems as well as the one that is

currently used by our group needs user input to select menu options and to trigger FES-

assisted stepping action. The current command interface for our FES system is a push-

button, which can be mounted on the walker or worn on a finger [1.4-1.7]. The push-

button as a command interface is plausible for selecting menu options during standing but

it is an impediment when it has to be actuated with fingers during walking to trigger

every step. Some individuals with limited finger and hand function find it difficult to

press push-buttons, more so while trying to maintain balance during ambulation. This

particular function of the push-button as a trigger for stepping action can be replaced by a

gait event detector. The gait event detector can identify the event (appropriate time during

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a gait cycle) to activate the required pattern of electrical stimulation. Some of the gait

event detectors investigated in past by other researchers are based on foot-switches,

accelerometers, gyroscopes, and the electromyogram (EMG)/electroneurogram (ENG)

[1.8-1.16]. The gait event detector based on motion sensors needs volitional movement of

limb segments which may not be possible with subjects with paralysis. We decided to

investigate electromyogram (EMG) since it temporally precedes the joint kinetics and

kinematics (electromechanical delay about 100 ms [1.17]) and may be feasible as a

control source even for individuals with incomplete spinal cord injury (iSCI), who may

have lost their ability to move but may still have volitionally controllable EMG activity

[1.18]. The natural latency between electrophysiological and biomechanical events

provides time to detect the intent and then assist the intended movement with FES. EMG-

based triggering of FES patterns should integrate the FES-generated movement

seamlessly with the volitional effort that is necessary in the case of iSCI individuals who

have some sensory and motor function below the level of injury.

The hardware for the FES-controller

The Universal External Control Unit (UECU) with 8-channel Implanted Receiver-

Stimulator (IRS-8) and 12-channel Implanted Stimulator-Telemeter (IST-12) were used

to implement the FES-controller to deliver electrical stimulation to the targeted muscles

[1.4]. The UECU controlled the temporal pattern of the stimulation that was transmitted

to the IRS-8 or IST-12 using an inductive coupling. A finger-switch connected to the

UECU served as a command interface to select menu options. Figure 1.1 shows the

components of the FES system. The UECU is typically 11cm x 8cm x 4.5cm and holds

four modules as shown in Figure 1.2 with its accessories.

The UECU contains the following internal modules as shown in Figure 1.3 [1.22],

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Communications module: serves as a communication hub for the UECU

and a central processor during stand-alone operation of the UECU. It also

contains circuitry for the power switch and inter-module bus. It is equipped

with a 16-bit processor, 1MB RAM, and 2MB of flash memory.

Implant control module: it has two radio frequency (6.78 MHz) channels to

communicate with two IRS or IST.

System module: it manages the user interface like push-buttons, a display,

and sound. It also has four analog inputs (two single-ended and two

differential) that are acquired by a 12-bit analog-to-digital converter.

Percutaneous stimulation module: it provides 12-channels of current-

controlled stimulation with maximum amplitude of 20mA and a compliance

voltage of 50V.

Surface stimulation module: it provides 4 or 8 channels of stimulation with

maximum amplitude of 100mA and a compliance voltage of 100V.

A unique address is assigned to each module for sending messages over inter-module

bus.

A Simulink (The Mathworks Inc., USA) toolkit is provided with blocksets for

programming the UECU. Real-time workshop (The Mathworks Inc., USA) is used to

generate the C code from the Simulink models that can then be compiled for real-time

execution in the communications module (Motorola HC(S) 12) or xPC target PC. An

FES-controller implemented in Simulink (The Mathworks Inc., USA) can be executed in

the communication module target in a stand-alone UECU or can be implemented in an

external target PC running xPC target (The Mathworks Inc., USA). A target PC running

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xPC target (The Mathworks Inc., USA) provides processing power and I/O capabilities

like data acquisition boards and serial ports. Figure 1.4 shows the stand-alone

configuration where the FES-controller is running in the communication module target in

the UECU. Figure 1.5 shows the UECU configuration with an xPC target PC. The

disadvantage of using an external xPC target PC is that the FES system is not portable.

The subject remains tethered to the external xPC target PC while using the FES system.

Electromyogram as a command source for FES-controller for ambulation after iSCI

The gait is roughly a cyclic process which can be divided into stepping of one side

followed by the other. A step defines the phase of the gait between foot-off that is the

instant when foot loses contact with the ground to the foot-off of the contralateral limb.

Gait is nevertheless a dynamic process where the steps dynamics are not isolated but one

step leads to the other steps in terms of the dynamics of the locomotor system. The

transition between the steps involves energy injection through push-off that generates a

burst of energy causing the foot to plantarflex and shifts the body towards the

contralateral limb and subsequently allowing the limb to swing forward. The push-off

correlates with a burst in the muscle activity over multiple synergist muscles, mainly the

ankle plantar-flexors. Electromyogram (EMG) is the time history of electrical activity in

the muscle that can be used to find the activation of the muscles. The burst in the muscle

activity during the push-off produces burst in the volitional EMG of all the synergist

muscles which have a pattern of activation during the transition phase of gait (i.e. left to

right step and right to left step transitions). This synergistic modulation of the volitional

EMG, if present in partially paralyzed muscles, can be used as a feature template to

identify the intent to transition from the left to right step and right to left step even when

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partially paralyzed muscles are too weak to produce enough moment at the joint to

produce effective push-off.

Prior work has shown that the EMG synergies found by principal component

analysis can provide information related to gait events and also gait-speed [1.19].

Transition specific EMG features can be identified using principal component analysis

which can then be used to identify the transition phase of the gait. A binary classifier to

trigger the transition from left to the right step and vice versa can be trained with the

parameters from correlation analysis of the EMG pattern with transition specific EMG

feature template. The correlation coefficients of the features associated with these

transitions are postulated to be clustered in the feature space. During online operation, the

classifier will have to identify the cluster from windowed EMG using cross-correlation

with the specified features and determine the intended transition. This method can be

conceptually extended to identify the transitions to other tasks like side-stepping, stair-

climbing, different gait-speeds etc. It is postulated that EMG-triggered FES-controller

will have an impact on the coordination of the FES-assisted iSCI gait. Seamlessly

integrating the FES-generated movement with the volitional movement should

significantly enhance the transitions from one gait phase to the other during walking.

There are challenges associated with the implementation of this method. The nature

of motor deficits in iSCI population is very heterogeneous. Some individuals can walk to

a certain extent with upper-body support, some can stand using the extensor tone and

some are completely non-ambulatory. The partially paralyzed muscles had to be selected

appropriately such that the volitional EMG from those muscles had enough information

to identify the gait phase transitions. The EMG had to be blanked during the stimulation

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to remove stimulation artifact that reduced the information content in the EMG. This may

produce overlapping clusters in EMG feature space that will be difficult to classify with

low false positive rate. More EMG channels (more than preferred two) may be needed in

order to reduce the false positive rate in that case. In this study, the enhanced

coordination during ambulation was investigated by dynamical systems tools like return

map analysis [1.20]. Subjective impressions of the two controllers were captured by a

Usability Rating Scale (URS) [1.21].

Electromyogram-based trigger for the FES-controller: specific objectives of the

work

The overall goal was to develop and evaluate an EMG-based trigger for the FES-

controller which can assist volitional motor function synergistically with electrical

stimulation during gait. The overall goal was divided into three specific aims.

Aim 1 - Muscle selection for EMG-based trigger: Select a set of two partially

paralyzed muscles in individuals with iSCI that yield consistent and reliable command

information for FES-assisted gait.

Hypothesis 1: The two partially paralyzed muscles will have volitionally

controllable EMG pattern similar to that in able-bodied individuals.

1. The iSCI subjects have volitional control over the surface EMG from the

partially paralyzed muscles that are comparable to able-bodied controls.

2. The iSCI subjects have EMG pattern in 2 partially paralyzed muscles with

enough information to identify the gait phase transitions during over-ground walking.

Aim 2 - Feasibility analysis of EMG-triggered FES-assisted ambulation:

Development and online-testing of a FES-controller for ambulation with a surface EMG-

based classifier for triggering FES-assisted steps in subjects with iSCI.

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Hypothesis 2: It was hypothesized that two muscles can be used to detect intention

for foot-off with false-positive rate less that 2 % and true-positive rate greater than 85 %.

Aim 3 - Evaluation of EMG-triggered FES-assisted gait: Compare the FES-

assisted gait with the surface EMG-triggered FES-controller with the switch-triggered

one with dynamical systems tools like return map analysis and subjective tools like

Usability Rating Scale to evaluate enhancement in coordination, especially during the

gait phase transitions.

Hypothesis 3: EMG-triggered FES-controller will enhance the FES-assisted over-

ground ambulation when compared to switch-triggered one.

Overview of the chapters

Chapter 2 addresses Hypothesis 1 and discusses the evaluation of surface

electromyogram from partially paralyzed muscles as a command source for triggering

FES-assisted steps during walking.

Chapter 3 addresses Hypothesis 2 and assesses the feasibility of triggering FES-

assisted steps with surface EMG-based classifier running in real-time during over-ground

ambulation.

Chapter 4 and 5 address Hypothesis 3 and compare EMG-triggered FES-assisted

gait to switch-triggered stepping. Chapter 4 discusses the gait parameters during over-

ground walking in the laboratory. Chapter 5 discusses the coordination and stability

during stand-to-walk transition in the laboratory.

Chapter 6 presents a proof-of-concept implementation of a simple binary classifier

based on intramuscular EMG from a completely implanted neuroprosthesis using

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methods developed in the earlier chapters for triggering FES-assisted steps with a fully

implantable FES system.

Chapter 7 discusses the challenges and the future work based on the results

presented in Chapters 2 to 6.

References

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1.2. W. T. Liberson, H. J. Holmquest, D. Scott, M.Dow, Functional electrotherapy:stimulation of the peroneal nerve synchronized with the swing phase of the gait of

hemiplegic patients,Arch Phys Med Rehabil, vol. 42, 1961, pp. 101-105.

1.3. R. Kobetic, R. J. Triolo, J. P. Uhlir, C. Bieri, M. Wibowo, G. Polando, E. B.Marsolais, J. A. Davis Jr., K. A. Ferguson, and M. Sharma, Implanted Functional

Electrical Stimulation System for Mobility in Paraplegia: A Follow-Up Case

Report,IEEE Trans. Rehabil. Eng., vol. 7, no. 4, Dec. 1999, pp. 390398.

1.4. B. Smith, Z. Tang, M.W. Johnson, S. Pourmehdi, M.M. Gazdik, J.R. Buckett, andP.H. Peckham, An externally powered, multichannel, implantable stimulator-telemeter for control of paralyzed muscle,IEEE Trans Biomed Eng., vol. 45, no.

4, 1998, pp. 463-475.

1.5. Z. Tang, B. Smith, J.H. Schild, and P.H. Peckham, Data transmission from an

implantable biotelemeter by load-shift keying using circuit configurationmodulator,IEEE Trans Biomed Eng., vol. 42, no. 5, 1995, pp. 525-528.

1.6. N. Bhadra, K.L. Kilgore, and P.H. Peckham, Implanted stimulators forrestoration of function in spinal cord injury,Med. Eng. Phys., vol. 23, 2001, pp.

19-28.

1.7. J. Knutson, M. Audu, and R. Triolo, Interventions for mobility and manipulationafter spinal cord injury: a review of orthotic and neuroprosthetic options, Topics

in Spinal Cord Rehab, in press.

1.8. J. R.W. Morris, Accelerometry a technique for the measurement of human bodymovements,J Biomech., vol. 6, 1973, pp. 72936.

1.9. I. P. Pappas, M. R. Popovic, T. Keller, V. Dietz, and M. Morari, A reliable gaitphase detection system,IEEE Trans. Neural Syst. Rehabil. Eng., vol. 9, no. 2,Jun. 2001, pp. 113-125.

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1.10. A. Mansfield, and G. M. Lyons, The use of accelerometry to detect heel contactevents for use as a sensor in FES assisted walking,Med. Eng. Phys., vol. 25, no.

10, Dec. 2003, pp. 879-885.

1.11. R. Williamson, and B. J. Andrews, Gait event detection for FES using

accelerometers and supervised machine learning,IEEE Transactions on

Rehabilitation Engineering, vol. 8, 2000, pp. 312319.

1.12. T. Sinkjaer, M. Haugland, A. Inman, M. Hansen, and K. D. Nielsen,Biopotentials as command and feedback signals in functional electrical

stimulation systems,Med. Eng. Phys., vol 25, no. 1, Jan. 2003, pp. 29-40.

1.13. R. T. Lauer, R. T. Smith, and R. R. Betz, Application of a neuro-fuzzy networkfor gait event detection using electromyography in the child with cerebral palsy,

IEEE Trans. Rehabil. Eng., vol. 52, no. 9, Sep. 2005, pp. 15321540.

1.14. D. Graupe, and H. Kordylewski, Artificial neural network control of FES in

paraplegics for patient responsive ambulation,IEEE Trans. Biomed Eng., vol.42, no. 7, Jul. 1995, pp. 699-707.

1.15. R. J. Triolo, and G. D. Moskowitz, The theoretical development of amultichannel time-series myoprocessor for simultaneous limb function detection

and muscle force estimation,IEEE Trans. Biomed Eng., vol. 36, no. 10, Oct.1989, pp. 1004-1017.

1.16. A. Dutta, R. Kobetic, and R. J. Triolo, EMG based triggering and modulation ofstimulation patterns for FES assisted ambulation a conceptual study, presented

at XXth Congress of the International Society of Biomechanics, Cleveland, OH,

Aug. 2005.

1.17. S. Zhou, M. F. Carey, R. J. Snow, D. L. Lawson, and W. E. Morrison, Effects ofmuscle fatigue and temperature on electromechanical delay,Electromyogr ClinNeurophysiol., vol 38, no. 2, Mar. 1998, pp. 67-73.

1.18. A. Dutta, and R. J. Triolo, Volitional surface EMG based control of FES-assistedgait after incomplete spinal cord injury a single case feasibility study,

presented at NIH Neural Interfaces Workshop, Bethesda, MD, Sep. 2005.

1.19. A. Hof, H. Elzinga, W. Grimmius, and J. Halbertsma, Speed dependence ofaveraged EMG pro-files in walking, Gait and Posture, vol. 16, 2002, pp. 7886.

1.20. Y. Hurmuzlu, and C. Basdogan, On the measurement of stability in humanlocomotion,ASME Journal of Biomechanical Engineering, vol. 116, 1994, pp.30-36.

1.21. E. Steinfeld, G. Danford, Eds.Enabling Environments: Measuring the Impact ofEnvironment on Disability and Rehabilitation. Kluwer/Plenum, 1999.

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1.22. Stephen Trier, UECU Toolkit Manual, Version 1.9, 2004.

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Figures

LaptopPC

ExternalControl Unit

CouplingCoil

In-LineConnectors

ImplantableReceiverStimulator

Electrodes

ClinicalInterface

Implanted components

Figure 1.1: Components of the FES system.

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Figure 1.2: Universal External Control Unit (UECU) with inductive coil and finger

switch.

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Figure 1.3: Universal External Control Unit (UECU) internal modules [1.22].

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Figure 1.4: Universal External Control Unit (UECU) stand-alone configuration [1.22].

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Figure 1.5: Universal External Control Unit (UECU) configuration with xPC target PC

[1.22].

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33

CHAPTER 2EVALUATION OF SURFACE ELECTROMYOGRAM FROM PARTIALLY

PARALYZED MUSCLES AS A COMMAND SOURCE FOR FUNCTIONAL

ELECTRICAL STIMULATION

A manuscript based on this chapter was submitted for publication in The Journal of

Rehabilitation Research & Development.

Abstract

Functional Electrical Stimulation (FES) facilitates ambulatory function after

paralysis by electrically activating the muscles of the lower extremities by exciting the

peripheral motor nerves. The FES-assisted stepping can be triggered by a manual switch

or by a gait event detector (GED). The objective of this study was to evaluate the

performance of the surface electromyogram (EMG) from partially paralyzed muscles for

detecting the intent to step during level over-ground walking. Two subjects with

incomplete spinal cord injuries (iSCI) and four able-bodied subjects volunteered for this

study. Subject iSCI-1 (age 23 years, C6 ASIA C) was non-ambulatory without the

assistance of FES. Subject iSCI-2 (age 34 years, T1 ASIA D) could walk only short

distances without FES. The four able-bodied subjects, Able-1 (age 26 years), Able-2 (age

25 years), Able-3 (age 25 years) and Able-4 (age 54 years) had no known injury or

pathology to either lower extremity during the study. Partially paralyzed muscles showed

performance similar (one-way two-tailed ANOVA, p


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sources for iSCI-1. left erector spinae with a mean DI of 0.93 for left step trigger and

right medial gastrocnemius with a mean DI of 0.88 for the right step trigger were the best

command sources for iSCI-2.

Introduction

Functional electrical stimulation (FES) provides an opportunity for brace-free

ambulation to wheelchair dependent individuals with incomplete spinal cord injuries

(iSCI). FES systems can electrically activate a customized set of muscles selected to

address individual gait deficits with pre-programmed patterns of stimulation to produce

cyclic movement of the lower extremities for ambulation [2.1], [2.2]. Users normally use

a switch to manually trigger each step and progress through the customized pattern of

stimulation to achieve walking function. In this study we evaluated the controllability

(the ability to volitionally modulate the surface electromyogram (EMG) in a visual

pursuit task) and discriminability (the ability to determine the intent to step during level

overground walking) of the surface EMG from both able-bodied volunteers and

individuals with iSCI. Our goal was to specify a process and criterion for selecting two

muscles for a new command and control interface that can be implemented with two

channels of implanted EMG recording electrodes with our next family of implantable

stimulator-telemeters (IST) [2.3-2.6]. This report summarizes the evaluation of the

surface EMG from partially paralyzed muscles of two subjects with iSCI and its

comparison with normative data from 4 able-bodied subjects.

While gait event detection is possible with physical sensors such as force sensitive

resistors, accelerometers, gyroscopes [2.7], [2.8], biopotentials such as EMG can also

provide useful and reliable information when the movement is impaired [2.9-2.11]. The

EMG temporally precedes the generation of force in a muscle and the resulting

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movement of a joint. This makes EMG an attractive signal for detection of intent and can

allow the desired movement to be assisted by FES. Graupe and Kordylewski presented a

neural network based classifier with on-line learning capabilities for individuals with

complete paraplegia [2.11], [2.12]. Thorsen et al. showed improved wrist extension with

stimulation controlled by surface EMG from partially paralyzed wrist extensors [2.13].

Futami et al. showed the feasibility of proportional control of FES with the surface EMG

from the same muscle (partially paralyzed knee extensors) in incomplete hemiplegia

[2.14]. Our preliminary study demonstrated the feasibility of FES-assisted walking

triggered by the surface EMG during double-support phase of gait (when both the feet are

on ground) [2.5]. A quantitative method is presented in this paper to evaluate the

electromyogram from partially paralyzed muscles as a command source for triggering

FES-assisted steps during ambulation.

Methods

Subjects

Two male subjects with incomplete spinal cord injury (iSCI) volunteered for this

study. iSCI-1 was a 23 years old male with C7 motor and C6 sensory incomplete spinal

cord injury (ASIA C) who could stand but could not initiate a step without the assistance

from FES. iSCI-2 was a 34 years old male with T1 motor and C6 sensory incomplete

spinal cord injury (ASIA D) who could walk only short distances without the assistance

from FES. They each received an 8 channel Implantable Receiver Stimulator (IRS-8) and

eight surgically implanted intramuscular electrodes in a related study designed to

facilitate household and limited community ambulation [2.15]. The four able-bodied

subjects, Able-1 (age 26 years), Able-2 (age 25 years), Able-3 (age 25 years) and Able-4

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(age 54 years) provided the normative data for comparison. They had no known injury or

pathology to either lower extremity during the course of the study.

The subject iSCI-1 received intramuscular stimulating electrodes bilaterally

recruiting iliopsoas, vastus intermedius and lateralis, tensor fasciae latae, tibialis anterior,

and peroneus longus muscles. The subject iSCI-2 received stimulation electrodes only on

his left side recruiting iliopsoas, vastus intermedius and lateralis, tensor fasciae latae,

gluteus medius, gluteus maximus, posterior portion of adductor magnus, and tibialis

anterior (2 electrodes). Temporal patterns of stimulation to activate the muscles were

customized for their particular gait deficits according to established tuning procedures in

order to achieve forward stepping in a rolling walker [2.16], [2.17]. The subjects

completed 6 weeks of over-ground gait training (2 hour sessions, 3 times per week) with

a physical therapist using the implanted FES system. After discharge from rehabilitation,

they volunteered for the studies using the myoelectric control of the FES system.

Informed consent was obtained from all the subjects before their participation and

all study related procedures were approved by the Institutional Review Board of the

Louis Stokes Cleveland Department of Veterans Affairs Medical Center.

Test of Controllability

Controllability in control theory means that the system states can be changed by

changing the system input and reachability means that there exists an input that changes

the states from A to B in finite time. Reachability always implies controllability. We will

define controllability for this experimental evaluation based on the definition of

reachability as the ability to modulate the EMG activity from one level to another in a

finite time during a visual pursuit task. The experimental setup for evaluating the

controllability of a muscle with biofeedback is shown in Figure 2.1. The surface EMG

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was collected from the rectus femoris while the subject was asked to track the absolute

value of a sinusoid of amplitude 0.7 and frequency 0.01 Hz over one time-period (i.e., the

TARGET signal) during a trial. The rectus femoris was maintained in an isometric

condition by Biodex System3 (Biodex Medical Systems, USA) dynamometer as shown in

Figure 2.1. The EMG was pre-amplified and low-pass filtered (anti-aliasing,

frequencycutoff=1000 Hz) by CED 1902 preamplifier (Cambridge Electronic Design,

England) before being sampled at 2200 Hz by the data-acquisition card (AT-MIO-64F-5,

National Instruments, USA) in a personal computer (PC). The data processing and

graphical display (GUI) were performed using Matlab R13 (The MathWorks, Inc., USA)

in the same PC. The EMG sampled by the data-acquisition card was band-pass filtered

(5th order zero-lag Butterworth, 20-500 Hz), de-trended and rectified before being

evaluated as a command signal (i.e., the TRACKING signal). The average EMG during

two seconds of maximum voluntary isometric contraction (MVC) was used for

normalization. The average magnitude of the EMG over two seconds while the subject

was asked to relax the muscle provided an estimate of the baseline activity. During visual

pursuit, the estimated baseline was subtracted from the EMG and then it was normalized

by the MVC. The normalized EMG was then divided into bins, each holding 0.1 sec of

data. The TRACKING signal (i.e., the processed EMG) pursuing the TARGET signal

was updated every 0.1 sec with the average value of the data in the latest bin only if the

mean was greater by twice the standard deviation, or less by one standard deviation, of

the data during MVC.

Both TARGET and TRACKING signals were projected on the wall in front of the

subject seated in the dynamometer. A set of five trials with a minimum five minutes of

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rest in between the trials were conducted on the left and right rectus femoris of the

subjects with iSCI. A set of five trials were conducted only on the right rectus femoris of

the right-handed able-bodied subjects. The absolute value of the difference between the

TARGET and TRACKING signals, the tracking error signal

( ERROR signal = TRACKING signal - TARGET signal ), was ensemble averaged over the set

of five trials. The trial period of 100 sec was divided into four parts of 25 sec each. The

first (0-25 sec) and the third (50-75 sec) parts were the periods during which the subject

was trying to contract the muscle to catch-up with the TARGET signal. The second (25-

50 sec) and the fourth parts (75-100 sec) were the periods when the subject was trying to

relax the muscle. The mean of the absolute tracking error was computed for each of these

four parts for comparison.

Test of Discriminability

Discriminability was defined as the ability to detect the intent to step with a binary

classifier using the surface EMG during the double-support phase of gait when both the

feet are in contact with the ground. Discriminability essentially indicated how well a

simple binary classifier could discriminate between the intent to step and the intent to

stand during the double-support phase of gait. Surface EMG signals were collected from

gluteus medius (GM), biceps femoris (BF), medial gastrocnemius (MG), rectus femoris

(RF), tibialis anterior (TA), and erector spinae (ES at T9) bilaterally. In case of iSCI

subjects, the surface EMG was collected during switch-triggered FES-assisted gait when

each step was initiated by depression of ring-mounted finger switch. The experimental

setup is shown in Figure 2.2 where subject is walking with an implanted switch-triggered

FES system based on an IRS-8 implanted pulse generator under the control of an external

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control unit (ECU). Surface EMG was collected using Ag/AgCl electrodes with 2 cm.

inter-electrode distance following the SENIAM guidelines [2.18]. The EMG signals were

amplified and low-pass filtered (anti-aliasing, frequencycutoff=1000 Hz) by CED 1902

amplifiers (Cambridge Electronic Design, England) before being sampled at 2400 Hz

(AT-MIO-64F-5, National Instruments, USA) in the host personal computer (PC). The

CED 1902 amplifier has a switching circuit (clamp) which was activated by a trigger

pulse that disconnected the electrode inputs from the amplifier and connected them to the

common electrode just before the start of the stimulation pulse. The input channels of

CED 1902 were clamped this way when stimulation pulses were applied to the muscles to

prevent stimulation artifact. The gain of each channel was set separately in the CED 1902

amplifiers to prevent saturation at the maximum muscle activity during the gait-cycle.

The implanted FES system (i.e., IRS-8) delivered electrical pulses at a frequency of 20

Hz, so the sampled EMG was divided into bins of 50ms duration. In each bin, 30ms

following the start of the stimulation pulse was blanked to remove the residual

stimulation artifact and M-wave, thus leaving signal related to voluntary muscle activity.

The remaining 20 ms of data in each bin was detrended, band-pass filtered (5th order

zero-lag Butterworth, 20-500 Hz), and rectified. The blanked portion of the EMG was

reconstructed with the average value of the EMG in the preceding and succeeding blocks

[2.19]. Then the whole EMG pattern was low pass filtered (5th order zero-lag

Butterworth, frequencycutoff=3 Hz) to get the linear envelope. The EMG pattern for each

muscle was normalized by the maximum value of the EMG linear envelope (LE) during a

gait cycle. The normalized LEs during a gait cycle were then divided into double-support

and swing phase of gait based on the occurrence of foot-strike and foot-off. The foot and

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ground contact sequences were determined from the insole foot switches (B&L

Engineering, USA) placed bilaterally at the medial and lateral heel, first and fifth

metatarsal, and big toe. The intent to step can be detected based on the magnitude of the

LE when it crosses a selected threshold (threshold-based) or by matching the LE pattern

with a specified pattern of muscle activity using cross-correlation analysis (pattern-

recognition).

The subjects were asked to start walking after standing for 3 sec and reach a self-

selected speed within 5m from the start position. After reaching the self-selected speed

the subjects had to decelerate and return to standing. The experimental protocol is shown

in Figure 2.3. The subjects were asked to wait in terminal stance for 3 sec. The

normalized LEs of each muscle were divided into two classes: the class True was

comprised of LEs (~ 150) during double-support phase prior to foot-off and the class

False consisted of the LEs (~150) during terminal stance and initial standing. Half of

the data were randomly allocated to training and used to find a characteristic pattern of

activation by ensemble averaging the LEs. The characteristic pattern found for the class

True was cross-correlated with the LEs from the other half of the data (test data) for the

classes True and False. A Receiver Operating Characteristics (ROC) curve shows

the tradeoff between sensitivity (True Positive Rate) and 1 specificity (False Positive

Rate) of a binary classifier [2.20]. The ROC curve was computed from the cross-

correlation coefficient (i.e., PRC for the pattern-recognition classifier) and the amplitude

(i.e., TC for the threshold-based classifier) of the LEs as the decision threshold was

varied over the range of data in the two classes, True and False. The LEs from all the

able-bodied subjects were pooled together. In case of able-bodied data, the left and the

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right sides were considered similar and the performance of the PRC and TC was

evaluated only for the right side. The ipsilateral muscles are the muscles of the right side

and contralateral muscles are the muscles of the left side for the classifiers (PRC and TC)

trying to detect the intent to step on the right side.

Discriminability Index (DIPRC and DITC) was defined as the area under the ROC

curve (AUC) which gave a measure of performance for the binary classifiers, PRC and

TC. Bradley showed that AUC exhibits a number of desirable properties when compared

to overall accuracy of the classifiers like increased sensitivity in Analysis of Variance

(ANOVA) tests standard error decreased as both AUC and the number of test samples

increased. AUC is also decision threshold independent and it is invariant to a priori class

probabilities [2.21]. The area under the ROC curve was numerically computed with

trapezoidal integration. Figure 2.4 illustrates the three cases,

where 1,15.0,5.00 DIDIDI . We are interested in 15.0 DI such that the mean

of the True data is greater than or equal to the mean of the False data and the values

greater than the discrimination threshold are classified as True.

The data were randomly partitioned ten times into training and test data-sets for a

10-fold cross-validation. For consistency the same training and test data-sets were used

by both the classifiers (PRC and TC) for the computation of the ROC curves in a paired

experimental design. Therefore, 10 ROC curves for each classifier were generated by

randomly pooling the LEs into training and test data-sets. The DI was computed for each

ROC curve and then averaged to find the mean (DIPRC and DITC) and standard deviation

(SD(DIPRC) and SD(DITC)) for each classifier (DIPRC for pattern-recognition classifier and

DITC for the threshold-based classifier) [2.21]. Wilcoxon statistic (W) was computed as

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an alias of DI (i.e., a performance measure of the classifier) to compare the two for

robustness. The standard error (SE(WPRC) and SE(WTC)) was computed from an

approximation of the Wilcoxon statistic (WPRC and WTC) which assumes exponential

distribution of the data in the classes, True and False. SE (W) has been shown to be

conservative as it overestimates the standard error [2.22]

)1(

2;

)2(

))(1())(1()1()(

2

21

2

2

2

1

W

WQ

W

WQ

CC

WQCWQCWWWSE

np

np

Where Cp and Cn are the number of data points in the classes, True and False

respectively.

Statistical Analysis

One-way two-tailed analysis of variance (anova1 in Matlab R14, The

MathWorks, Inc., USA) was performed on the absolute tracking error that was obtained

from the Test of Controllability. All observations were considered to be mutually

independent for the ANOVA test. The p-value was computed for the null hypothesis that

the absolute tracking error parameter has the same mean for all the cases. If the p-value

was close to zero (


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the performance measure for all the muscles have equal means, 3 there is no

interactions between the classifier type and muscle type. If the p-value was close to zero

(


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shows that all the subjects (iSCI and able-bodied) performed similarly in the visual

pursuit task for the Test of Controllability. Individuals with iSCI were able to control the

contraction of their muscles equally well as able-bodied individuals.

The average absolute tracking error was smallest (mean = 5.48) in the first part (0-

25 sec) of the trial period, for the subjects with iSCI, corresponding to the initial period of

increasing isometric contraction. There was a slight deterioration in the performance of

the iSCI subjects in the third part of the trial, corresponding to the second period of

increasing contraction (50-75 sec, mean=7.96) when compared to the first part (0-25 sec,

mean=5.48). The subjects with iSCI performed worse in the second (25-50 sec,

mean=9.11) and fourth (75-100 sec, mean=10.27) parts of the trial period, which required

relaxing the muscle in a controlled fashion.

Results from the Test of Discriminability

Table 2.2 shows the results from the Test of Discriminability for the muscles

gluteus medius (GM), biceps femoris (BF), medial gastrocnemius

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