adutta dissertation

8/8/2019 ADUTTA Dissertation

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DEVELOPMENT OF AN ELECTROMYOGRAM-BASED CONTROLLER FOR

FUNCTIONAL ELECTRICAL STIMULATION-ASSISTED WALKING AFTER PARTIALPARALYSIS

By

ANIRBAN DUTTA

Dissertation Advisor: Dr. Ronald J. Triolo

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOLOF 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

August, 2008


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To the Almighty, Baba, Ma, Kiran, and Rachna


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TABLE OF CONTENTS

page

LIST OF TABLES...........................................................................................................................6

LIST OF FIGURES .........................................................................................................................8

Introduction....................................................................................................................................13

Functional Electrical Stimulation (FES) for ambulation........................................................13Electromyogram as a command source for FES-controller for ambulation after iSCI ..........14

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

work .............................................................................................................................16

Overview of the chapters........................................................................................................17References...............................................................................................................................17

Evaluation of surface electromyogram from partially paralyzed muscles as a commandsource for functional electrical stimulation ............................................................................20

Abstract...................................................................................................................................20

Introduction.............................................................................................................................21

Methods ..................................................................................................................................22Subjects............................................................................................................................22

Test of Controllability .....................................................................................................23

Test of Discriminability...................................................................................................24

Statistical Analysis ..........................................................................................................28

Results.....................................................................................................................................29 Results from the Test of Controllability..........................................................................29

Results from the Test of Discriminability .......................................................................30

Discussion...............................................................................................................................32

Conclusion ..............................................................................................................................33

References...............................................................................................................................34

Figures ....................................................................................................................................37

Tables......................................................................................................................................46

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

spinal cord injury ....................................................................................................................50

Abstract...................................................................................................................................50

Introduction.............................................................................................................................50

Methods ..................................................................................................................................52Subjects............................................................................................................................52

Data Acquisition and Processing.....................................................................................53

Muscle Selection .............................................................................................................55

Classifier Development and Offline Testing...................................................................56

Classifier Testing During FES-assisted Ambulation.......................................................58

Results.....................................................................................................................................59


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Classifier Performance ....................................................................................................59

Repeatability of the Classifier Performance....................................................................60Discussion...............................................................................................................................60

Conclusion ..............................................................................................................................62References...............................................................................................................................63

Figures ....................................................................................................................................66

Surface EMG-triggered FES-assisted gait parameters during over-ground walking in thelaboratory................................................................................................................................73

Abstract...................................................................................................................................73

Introduction.............................................................................................................................74

Methods ..................................................................................................................................75

Subjects............................................................................................................................75

Gait Data Acquisition......................................................................................................76Gait Parameters ...............................................................................................................78

Statistical Analysis ..........................................................................................................79

Results.....................................................................................................................................80 Discussion...............................................................................................................................81

Conclusion ..............................................................................................................................83

References...............................................................................................................................84

Figures ....................................................................................................................................85

Tables......................................................................................................................................90

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

the laboratory..........................................................................................................................92

Abstract...................................................................................................................................92

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

Methods ..................................................................................................................................95

Subjects............................................................................................................................95 Gait Data Acquisition......................................................................................................95

Coordination and Stability Analysis of Gait initiation ....................................................98

Results...................................................................................................................................103

Linear regression model for gait initiation ....................................................................103

Coordination and stability during FES-assisted gait initiation......................................104

Discussion.............................................................................................................................105 Conclusions...........................................................................................................................109

References.............................................................................................................................110Figures ..................................................................................................................................113

Development of an implanted intramuscular EMG-triggered FES-system for ambulationafter incomplete spinal cord injury.......................................................................................123

Abstract.................................................................................................................................123 Introduction...........................................................................................................................124

Methods ................................................................................................................................126


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Subjects..........................................................................................................................126

Command source selection............................................................................................127Implantation of intramuscular EMG electrode..............................................................129

Classifier development for iEMG-triggered FES-assisted stepping .............................130Online testing of the classifier in the laboratory ...........................................................134

Results...................................................................................................................................136

Muscles and location selection for intramuscular EMG ...............................................136

Classifier development and online performance ...........................................................137

Discussion.............................................................................................................................142

Conclusions...........................................................................................................................145

References.............................................................................................................................146

Figures ..................................................................................................................................149Tables....................................................................................................................................163


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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, 75-100 sec) of the Testfor Controllability. The p-value from the one-way two-tailed ANOVA test for the

average tracking error over the whole trial (100 sec) was not statistically significant( 0.01)...............................................................................................................................46

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

(GM), Biceps Femoris (BF), Medial Gastrocnemius (MG), Rectus Femoris (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) ofthe 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 themuscle type as well as the classifier type...........................................................................47

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 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 classifier type.....................................................................48

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 classifier type.....................................................................48

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 the DI and the Standard Error

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

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

as the classifier type. ..........................................................................................................49

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 DI and

the Standard Error (SE) found for the Wilcoxon statistic (W) were similar. Therewere statistically significant (p 0.05) differences in the means of DI due to the

muscle type as well as the classifier type...........................................................................49


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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-triggered andswitch-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), leftswing 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 2.1: Experimental setup for the Test of Controllability of the surface EMG from

Rectus Femoris using visual pursuit tasks while the knee is fixed in a dynamometer. .....37

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

assisted overground walking..............................................................................................38

Figure 2.3: Experimental protocol for surface EMG data collection during overground

walking, where the subject had to start from standing and achieve a self-selected gaitspeed within 5m. ................................................................................................................39

Figure 2.4: The left column shows the cumulative distribution function for the three cases,1,15.0,5.00 =


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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.......................44

Figure 2.9: Top panel shows the results from the post hoc analysis of the DiscriminabilityIndex with their critical values from Scheffes S procedure for 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 the left and right step classifiers of iSCI-2. The bottom panel


critical values from Scheffes S procedure for different classifiers Pattern

Recognition Classifier (PRC) and Threshold-based Classifier (TC) obtained from the

Test of Discriminability of the left and the right step classifiers of iSCI-2.......................45

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 ..........................66

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)..................................................................................................................67

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

EMG signal ........................................................................................................................68

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

from switch-triggered FES-assisted gait data (FS: Foot-Strike, FO: Foot-Off) a)

linear envelope (LE) indicating class True b) linear envelope (LE) indicating class

False ................................................................................................................................69

Figure 3.5: Receiver operating characteristics curve of the classifiers using the test data ............70

Figure 3.6: State transition diagram of the EMG-based FES-controller .......................................71

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 phase following left swingphase, Right DS: double support phase following right swing phase, SW: swing

phase) .................................................................................................................................72

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). ...........85

Figure 4.2: EMG-based gait event detector for triggering FES-assisted steps. ............................86

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-off frequencies to find the


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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. ...................................................87

Figure 4.4: Gait data collection protocol in laboratory conditions where the subject had tostart from standing and achieve a self-selected gait speed within Vicon

TMvolume of

data capture (~5m). ............................................................................................................88

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 themean of that during EMG-triggered trials of iSCI 2. The box shows the lower

quartile, median, and upper quartile with whiskers extending at each end showing the

range of the data. The notches around the median show the estimate of the

uncertainty. The boxes whose notches dont overlap indicate that their medians differ

at 5% significance level. ....................................................................................................89

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.......113

Figure 5.2: Top panel: Selection of optimum cut-off frequencies for low-pass filtering 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...................................................................................................................................114

Figure 5.3: Gait initiation protocol during the data collection.....................................................115

Figure 5.4: Typical pelvis motion in the direction of progression during gait initiation.............116

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].................................................................................................................................117

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..........................118

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 the weight

matrix W of the subject Able1. The prefix l indicates the left side and r indicatesthe right side. The suffix x denotes sagittal plane, y denotes frontal plane, and z

denotes transverse plane for the joint angles. ..................................................................119

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


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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] ............120

Figure 5.9: Top panel: Scatter plot of QoF and Av. Eig. at 6 gait events for the groups; the 4able-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 ..........................................................121

Figure 5.10: Mahalanobis distances matrix between each pair of group means .........................122

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)...149

Figure 6.2: Processing of the sampled surface EMG a) rectified and reconstructed sEMG

signal b) linear envelope found from processed sEMG signal. .......................................150

Figure 6.3: Experimental protocol for the collection of EMG data during over-groundwalking in the laboratory. ................................................................................................151

Figure 6.4: Multi-electrode matrix for simultaneous collection of the surface EMG frommultiple locations on the muscle belly.............................................................................152

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 the polymer sheath leaving

the iEMG electrode in place. ...........................................................................................153

Figure 6.6: Pulse-width map of the stimulation patterns used for walking shown as an

example............................................................................................................................154

Figure 6.7: The real-time cycle in IST with 50 ms time period for stimulation frequency of

20 Hz................................................................................................................................155

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

collected with the switch-triggered FES system..............................................................156

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

walking.............................................................................................................................157

Figure 6.10: Usability Rating Scale to find the user perspective on ease/difficulty of using

the classifier [6.29]...........................................................................................................158

Figure 6.11: Best location found from the surface EMG for implanting intramuscular EMG

electrodes a) Left gastrocnemius and right erector spinae b) Left and right

gastrocnemius. .................................................................................................................159

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

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


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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. ................................................................................................................................160

Figure 6.13: Inhibition of iEMG from right erector spinae during right swing phase (SW) asshown in the top panel due to electrical stimulation of the same muscle when

compared to that in absence of electrical stimulation shown in the bottom panel ofthe subject iSCI-1.............................................................................................................161

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 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 ofthe gait cycle. ...................................................................................................................162


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CHAPTER 1

INTRODUCTION

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 a gait cycle) to activate the required

pattern of electrical stimulation. Some of the gait event detectors investigated in past by other


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researchers are based on foot-switches, accelerometers, gyroscopes, and the electromyogram

(EMG)/electroneurogram (ENG) [1.8-1.16]. 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.

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 acitivity 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,


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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 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 incomplete SCI 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 to remove stimulation


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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.

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 %.


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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 methods developed

in the earlier chapters for triggering FES-assisted steps with a fully implantable FES system.

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1.12.T. Sinkjaer, M. Haugland, A. Inman, M. Hansen, and K. D. Nielsen, Biopotentials ascommand 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 network for gaitevent detection using electromyography in the child with cerebral palsy, IEEE Trans.

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


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1.14.D. Graupe, and H. Kordylewski, Artificial neural network control of FES in paraplegicsfor 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 a multichannel time-series myoprocessor for simultaneous limb function detection and muscle forceestimation, 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 of musclefatigue and temperature on electromechanical delay, Electromyogr Clin Neurophysiol.,

vol 38, no. 2, Mar. 1998, pp. 67-73.

1.18.A. Dutta, and R. J. Triolo, Volitional surface EMG based control of FES-assisted gait afterincomplete 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 of averagedEMG 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 human locomotion,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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CHAPTER 2

EVALUATION OF SURFACE ELECTROMYOGRAM FROM PARTIALLY PARALYZEDMUSCLES AS A COMMAND SOURCE FOR FUNCTIONAL ELECTRICAL

STIMULATION

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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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 [2.9-2.11]. The EMG temporally precedes the generation of force

in a muscle and the resulting 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


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[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 (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


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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 was defined 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 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.


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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 in the preceding bin.

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 5 minutes of 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 using the surface

EMG during the double-support phase of gait when both the feet are in contact with the ground.

Surface EMG signals were collected from gluteus medius (GM), biceps femoris (BF), medial


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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 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


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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 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


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together. In case of able-bodied data, the left and the 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 =


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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 MatlabTM

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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was considered statistically significant. To find which pairs were significantly different, post hoc

tests were performed with the critical values found from Scheffes S procedure.

ANOVA is insensitive to departures from the assumption of equal variances when the

sample sizes are equal, as in our case. Moreover, prior work has shown that ANOVA is robust

to violations of its assumptions [2.23]

Results

Results from the Test of Controllability

The TRACKING signal (broken black line) using the surface EMG of rectus femoris and

the TARGET signal (solid black line) that was the absolute value of a 0.01 Hz sinusoid of

amplitude 0.7 during visual pursuit over 100 ms is shown in Figure 2.5 and 2.6. The top panel of

Figure 2.5 shows the results for the rectus femoris of the left and the right sides of iSCI-1 and the

bottom panel shows the same for iSCI-2. Figure 2.6 shows the results for surface EMG from

rectus femoris of the right side for the able-bodied subjects. The solid black line is the TARGET

signal and the broken black line shows the TRACKING signal that was ensemble averaged over

5 trials. 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. Table 2.1 presents the mean, the minimum, and the maximum average

absolute tracking error during four parts (0-25 sec, 25-50 sec, 50-75 sec, 75-100 sec) of the trial.

The p-value from the one-way two-tailed ANOVA test for the average tracking error over the

whole trial (100 sec) was not statistically significant ( 0.01). This 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.


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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 (MG), rectus femoris (RF), tibialis

anterior (TA), and erector spinae (ES at T9) 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 type as well as

the classifier type. The results from the post hoc analysis are presented in Figure 2.7. The top

panel of Figure 2.7 shows that the Ipsilateral MG, Ipsilateral ES, Contralateral BF, Contralateral

GM, and Contralateral TA in black markers that have performed the best (mean DI=1) as a

command source in the Test of Discriminability. The bottom panel of Figure 2.7 shows that the

Pattern Recognition Classifier (mean DIPRC=0.7586) performed much better than the Threshold-

based Classifier (mean DITC=0.5016).

Table 2.3a and b show the results from the Test of Discriminability of iSCI-1 for the left

step and right step classifiers respectively. The Wilcoxon statistic (W) was similar in magnitude

to the corresponding value of the Discriminability Index (DI). Similarly the Standard Deviation


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(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 classifier type. The results from post hoc analysis are presented in

Figure 2.8. The top panel first column of Figure 2.8 shows that Left ES (mean DI=0.8705) in

black marker performed the best followed by Left MG (mean DI=0.7881) in gray marker in the

Test of Discriminability for the left step. The bottom panel first column of Figure 2.8 shows that

Pattern Recognition Classifier (mean DIPRC=0.6486) in black marker performed slightly better

than the Threshold-based Classifier (mean DITC=0.6071) in gray marker in the Test of

Discriminability for the left step. The top panel second column of Figure 2.8 shows that Right ES

(mean DI=0.8299) performed the best followed by Right MG (mean DI=0.7989) in the Test of

Discriminability for the right step. The bottom panel first column of Figure 2.8 shows that

Pattern Recognition Classifier (mean DIPRC=0.6559) in black marker performed slightly better

than the Threshold-based Classifier (mean DITC=0.5886) in gray marker in the Test of

Discriminability for the right step.

Table 2.4a and b show the results from the Test of Discriminability of iSCI-2 for the left

step and right step classifiers respectively. The Wilcoxon statistic (W) and the corresponding

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

and the Standard Error (SE) found for the Wilcoxon statistic (W) were similar. There were

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

the classifier type. The results from post hoc analysis are presented in Figure 2.9. The top panel

first column of Figure 2.9 shows that Left ES (mean DI=0.9293) in black marker performed the

best followed by Left MG (mean DI=0.8736) and Right RF (mean DI=0.8536 in the Test of

Discriminability for the left step. The bottom panel first column of Figure 2.9 shows that Pattern


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Recognition Classifier (mean DIPRC=0.71856) in black marker performed much better than the

Threshold-based Classifier (mean DITC=0.45457) in gray marker in the Test of Discriminability

for the left step. The top panel second column of Figure 2.9 shows that Right MG (mean

DI=0.8805) performed the best followed by Right ES (mean DI=0.853) and Left RF (mean

DI=0.8484) in the Test of Discriminability for the right step. The bottom panel first column of

Figure 2.9 shows that Pattern Recognition Classifier (mean DIPRC=0.6853) in black marker

performed slightly better than the Threshold-based Classifier (mean DITC=0.54613) in black

marker in the Test of Discriminability for the right step.

Discussion

The controllability of the surface EMG from partially paralyzed rectus femoris of subjects

with incomplete SCI was evaluated in a visual pursuit task and found to have performance

similar (p


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for classification in the Test of Discriminability, where the muscles which had 15.0


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implanted stimulator-telemeter that acquire and transmit EMG information from implanted EMG

electrodes. The new family of implantable stimulator-telemeter (IST-12) has only 2 implanted

EMG channels and the capability to perform the signal processing required by the classifier

[2.6]. Partially paralyzed muscles showed controllability similar (p


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2.8. A. Mansfield, and G. M. Lyons, The use of accelerometry to detect heel contact events foruse as a sensor in FES assisted walking, Med. Eng. Phys., vol. 25, no. 10, Dec. 2003, pp.879-885.

2.9. T. Sinkjaer, M. Haugland, A. Inman, M. Hansen, and K. D. Nielsen, Biopotentials ascommand and feedback signals in functional electrical stimulation systems, Med. Eng.Phys., vol 25, no. 1, Jan. 2003, pp. 29-40.

2.10.R. T. Lauer, R. T. Smith, and R. R. Betz, Application of a neuro-fuzzy network for gaitevent detection using electromyography in the child with cerebral palsy, IEEE Trans.

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

2.11.D. Graupe, and H. Kordylewski, Artificial neural network control of FES in paraplegicsfor patient responsive ambulation, IEEE Trans. Biomed Eng., vol. 42, no. 7, Jul. 1995, pp.

699-707.

2.12.H. Kordylewski, and D. Graupe, Control of Neuromuscular Stimulation for Ambulationby Complete Paraplegics via Artificial Neural Networks, Neurol. Research, vol. 23, July

2001, pp.472-481.

2.13.R. Thorsen, R. Spadone, and M. Ferrarin, A pilot study of myoelectrically controlled FESof upper extremity, IEEE Trans. Rehabil. Eng., vol. 9, no. 2, June 2001, pp. 161167.

2.14.R.Futami, K.Seki, T.Kawanishi, T.Sugiyama, I.Cikajlo, and Y.Handa, Application of localEMG-driven FES to incompletely paralyzed lower extremities, presented at 10th Annual

Conference of the International FES Society, Montreal, Canada, July 2005.

2.15.E. Hardin, R. Kobetic, L. Murray, M. Corado-Ahmed, G. Pinnault, J. Sakai, S. Nogan, C.Ho, and R. Triolo, Ambulation after incomplete spinal cord injury with an implanted FESsystem: a case report, Jour. Rehab R&D, 44:3, 2007, pp. 333-346.

2.16.R. Kobetic, and E.B. Marsolais, Synthesis of paraplegic gait with multichannel functionalneuromuscular stimulation, IEEE Trans Rehab Eng., vol. 2, no. 2, 1994, pp. 66-79.

2.17.R. Kobetic, R. J. Triolo, and E. B. Marsolais, Muscle selection and walking performanceof multichannel FES systems for ambulation in paraplegia, IEEE Trans. Rehabil. Eng.,vol. 5, no. 1, Mar. 1997, pp. 2329.

2.18.H. J. Hermens, B. Freriks, R. Merletti, D. Stegeman, J. Blok, G. Rau, C. Disselhorst-Klug,and G. Hagg, SENIAM 8 European Recommendations for Surface ElectroMyoGraphy.

Enschede, Netherlands: Roessingh Research and Development, 1999.

2.19.A. E. Hines, P. E. Crago, G. J. Chapman, and C. Billian, Stimulus artifact removal inEMG from muscles adjacent to stimulated muscles, J. Neurosci. Methods, vol. 64, no. 1,

Jan. 1996, pp. 55-62.

2.20.T. D. Dickens, Elementary Signal Detection Theory. US: Oxford University Press, 2001,pp. 66, 121.


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2.21.A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machinelearning algorithms,"Pattern Recognition, vol. 30, no. 7, 1997, pp. 1145-1159.

2.22.J. A. Hanley and B. J. McNeil, "The meaning and use of the area under a receiver operatingcharacteristic (ROC) curve," Radiology, vol. 143, 1982, pp. 29-36.

2.23.R. E. Walpole and R. H. Myers, Probability and Statistic for Engineers and Scientists.Macmillan, New York, 1990


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Figures

Figure 2.1: Experimental setup for the Test of Controllability of the surface EMG from Rectus

Femoris using visual pursuit tasks while the knee is fixed in a dynamometer.


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

assisted overground walking.


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Figure 2.3: Experimental protocol for surface EMG data collection during overground walking,

where the subject had to start from standing and achieve a self-selected gait speed

within 5m.


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

1,15.0,5.00 =


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Figure 2.5: TRACKING (broken black line) and TARGET (solid black line) signals duringvisual pursuit task for the Test of Controllability. The boxes at each data point showthe 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 results for 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.


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Figure 2.6: TRACKING (broken black line) and TARGET (solid black line) signals duringvisual pursuit task for the Test of Controllability with able-bodied subjects. The boxes

at each data point show the lower quartile and upper quartile values of theTRACKING signal. Whiskers extending at the top and bottom of the boxes show the

range of the TRACKING signal.


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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 Gastrocnemius (MG), Rectus Femoris

(RF), Tibialis Anterior (TA), and Erector Spinae (ES at T9) obtained from the Test ofDiscriminability 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 Test of Discriminability withable-bodied subjects.


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Figure 2.9: 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 Gastrocnemius (MG), Rectus Femoris

(RF), Tibialis Anterior (TA), and Erector Spinae (ES at T9) obtained from the Test of

Discriminability of the left and right step classifiers of iSCI-2. The bottom panel


critical values from Scheffes S procedure for different classifiers Pattern

Recognition Classifier (PRC) and Threshold-based Classifier (TC) obtained from theTest of Discriminability of the left and the right step classifiers of iSCI-2.


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Tables

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, 75-100 sec) of the Test forControllability. The p-value from the one-way two-tailed ANOVA test for the

average tracking error over the whole trial (100 sec) was not statistically significant( 0.01).

%MVCPart 1

(0-25 sec)Part 2

(25-50 sec)Part 3

(50-75 sec)Part 4

(75-100 sec)

Mean Min Max Mean Min Max Mean Min Max Mean Min Max

iSCI-1 Left RF 5.34 0.01 11.6 10.68 1.24 25.8 8.98 0.16 21.39 16.05 1.02 26.97iSCI-1 Right

RF 6.57 0.57 15.02 9.4 1.44 20.61 8.49 0.44 20.97 9.58 0.59 21.04iSCI-2 Left RF 7.18 0.45 16.42 9.84 0.14 32.31 9.75 0.33 33.1 9.23 0.28 26.18iSCI-2 RightRF 2.81 0.02 5.77 6.52 0.34 16.9 4.63 0.05 9.4 6.23 0.66 15.55Able-1 RightRF 4.05 0.29 9.5 2.42 0.02 7.86 3.29 0.42 10.92 3.26 0.02 10.77Able-2 RightRF 8.98 5.28 13.19 4.99 0.09 14.49 7.75 0.07 23.11 13.19 0.06 26.8Able-3 RightRF 4.79 0.11 9.49 5.11 0.02 13.63 3.51 0.18 10.37 2.46 0.02 8.11Able-4 RightRF 12.89 4.56 18.71 6.66 0.32 15.76 5.93 0.2 13.71 6.66 0.81 11.72


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Table 2.2: The results from the Test of Discriminability for the muscles Gluteus Medius (GM),

Biceps Femoris (BF), Medial Gastrocnemius (MG), Rectus Femoris (RF), TibialisAnterior (TA), and Erector Spinae (ES at T9) are presented for the able-bodied

subjects. The Wilcoxon statistic (W) was similar in magnitude to the correspondingDiscriminability 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 type as well as

the classifier type.

Muscles for right stepclassifier for able-bodiedsubjects

DIPRCSD

(DIPRC)WPRC

SE(WPRC)

DITCSD

(DITC)WTC

SE(WTC)

Ipsilateral GM 0.42 0 0.43 0 0 0 0 0Ipsilateral BF 0.29 0 0.3 0 0 0 0 0

Ipsilateral MG 1 0 1 0 1 0 1 0

Ipsilateral RF 0.74 0.01 0.75 0.01 0.59 0.05 0.59 0.05

Ipsilateral TA 0.52 0 0.54 0 0 0 0 0

Ipsilateral ES 1 0 1 0 1 0 1 0

Contralateral GM 1 0 1 0 1 0 1 0

Contralateral BF 1 0 1 0 1 0 1 0

Contralateral MG 0.26 0 0.28 0 0 0 0 0

Contralateral RF 1 0 1 0 0.42 0.04 0.42 0.04

Contralateral TA 1 0 1 0 1 0 1 0

Contralateral ES 0.49 0.02 0.49 0.01 0 0 0 0


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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 theDiscriminability 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 classifier type.Muscles for left stepclassifier of iSCI-1 DIPRC

SD(DIPRC) WPRC

SE(WPRC) DITC

SD(DITC) WTC

SE(WTC)

Left GM 0.77 0.06 0.78 0.06 0.66 0.06 0.67 0.06

Left BF 0.56 0.03 0.56 0.03 0.57 0.05 0.58 0.05

Left MG 0.81 0.02 0.81 0.02 0.77 0.06 0.77 0.06

Left RF 0.72 0.04 0.73 0.04 0.57 0.05 0.57 0.05

Left TA 0.63 0.08 0.64 0.08 0.58 0.06 0.58 0.06

Left ES 0.96 0.04 0.96 0.038 0.78 0.06 0.79 0.06

Right GM 0.5 0.04 0.5 0.039 0.53 0.03 0.55 0.03Right BF 0.56 0.04 0.57 0.036 0.56 0.05 0.56 0.05

Right MG 0.58 0.04 0.59 0.039 0.56 0.06 0.56 0.06

Right RF 0.65 0.08 0.67 0.075 0.53 0.06 0.54 0.05

Right TA 0.56 0.05 0.56 0.045 0.55 0.06 0.56 0.06

Right ES 0.51 0.06 0.51 0.057 0.60 0.05 0.61 0.05

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 tothe muscle type as well as the classifier type.

Muscles for right stepclassifier of iSCI-1 DIPRC

SD(DIPRC) WPRC

SE(WPRC) DITC

SD(DITC) WTC

SE(WTC)

Left GM 0.48 0.07 0.49 0.07 0.57 0.06 0.57 0.059

Left BF 0.55 0.07 0.56 0.07 0.58 0.04 0.59 0.04

Left MG 0.68 0.06 0.68 0.06 0.59 0.04 0.6 0.04

Left RF 0.66 0.05 0.67 0.05 0.55 0.05 0.55 0.05

Left TA 0.53 0.05 0.53 0.05 0.56 0.07 0.57 0.07

Left ES 0.57 0.05 0.57 0.05 0.55 0.05 0.56 0.04

Right GM 0.77 0.04 0.78 0.04 0.53 0.03 0.53 0.03

Right BF 0.52 0.04 0.53 0.04 0.52 0.06 0.53 0.06

Right MG 0.85 0.05 0.86 0.05 0.75 0.05 0.75 0.05

Right RF 0.69 0.05 0.7 0.05 0.53 0.05 0.54 0.05

Right TA 0.68 0.04 0.68 0.04 0.56 0.05 0.57 0.06

Right ES 0.89 0.06 0.89 0.06 0.77 0.04 0.77 0.05


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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 the DI and the Standard Error (SE)

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



Muscles for left stepclassifier of iSCi-2 DIPRC

SD(DIPRC) WPRC

SE(WTC) DITC

SD(DITC) WTC

SE(WTC)

Left GM 0.49 0.07 0.49 0.062 0 0 0 0

Left BF 0.47 0.026 0.47 0.026 0 0 0 0

Left MG 0.99 0 0.99 0 0.743 0.06 0.74 0.06

Left RF 0.46 0.041 0.46 0.04 0.68 0.028 0.68 0.024Left TA 0.47 0.0042 0.48 0.004 0 0 0 0

Left ES 0.99 0 0.99 0 0.87 0.034 0.87 0.04

Right GM 0.83 0.002 0.83 0.002 0.71 0.027 0.70 0.027

Right BF 0.87 0.003 0.89 0.003 0.6 0.02 0.59 0.02

Right MG 0.83 0.008 0.84 0.008 0.69 0.022 0.69 0.02

Right RF 0.99 0 0.99 0 0.71 0.02 0.70 0.01

Right TA 0.82 0.013 0.82 0.012 0.48 0.02 0.48 0.02

Right ES 0.41 0.075 0.41 0.075 0 0 0 0

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 DI and the Standard Error (SE)found for the Wilcoxon statistic (W) were similar. There were statistically



Muscles for right stepclassifier of iSCI-2 DIPRC

SD(DIPRC) WPRC

SE(WPRC) DITC

SD(DITC) WTC

SE(WTC)

Left GM 0.74 0.003 0.74 0.003 0.69 0.017 0.698 0.017

Left BF 0.88 0.012 0.87 0.012 0.59 0.029 0.598 0.028

Left MG 0.68 0.007 0.69 0.007 0.66 0.016 0.67 0.016

Left RF 0.99 0.003 0.99 0.003 0.69 0.020 0.698 0.02

Left TA 0.68 0.019 0.69 0.016 0.7 0.019 0.699 0.016

Left ES 0.49 0.014 0.51 0.014 0.57 0.018 0.57 0.017

Right GM 0.40 0.017 0.47 0.015 0 0 0 0

Right BF 0.37 0.018 0.37 0.018 0 0 0 0

Right MG 0.99 0 0.99 0.001 0.75 0.056 0.75 0.055

Right RF 0.59 0.007 0.61 0.006 0.56 0.037 0.57 0.037

Right TA 0.41 0.019 0.41 0.017 0.58 0.019 0.59 0.017

Right ES 0.99 0.001 0.99 0.001 0.71 0.045 0.71 0.46


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CHAPTER 3

FEASIBILITY ANALYSIS OF SURFACE EMG-TRIGGERED FES-ASSISTEDAMBULATION AFTER INCOMPLETE SPINAL CORD INJURY

A part of this chapter was published in IEEE Trans Biomed Eng. 2008 Feb; 55(2).

Abstract

Ambulation after spinal cord injury is possible with the aid of functional electrical

stimulation (FES). Individuals with incomplete spinal cord injury (iSCI) retain partial volitional

control of muscles below the level of injury, necessitating careful integration of FES with intact

voluntary motor function for efficient walking. In this study, the surface electromyogram (EMG)

of the volitionally controlled erector spinae was used to detect the intent to step and trigger FES-

assisted walking in a volunteer with iSCI via an 8-channel implanted stimulation system. The

classifier was able to trigger the FES-assisted swing-phase of gait with a false positive rate less

than 1% and true positive rate greater than 82% during over-ground ambulation on a level

surface. The performance of the EMG classifier highlights its potential as a natural command

interface to better coordinate stimulated and volitional muscle activities than conventional

manual switches and facilitate FES-assisted community ambulation.

Introduction

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

to wheelchair dependent individuals with incomplete spinal cord injuries (iSCI). The implanted

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. Our 8-channel implantable receiver-stimulator (IRS-8) delivers

stimulation via implanted electrodes to the targeted motor nerves activating the muscles required

to produce stepping motions [3.1]. Power and stimulus control information are transmitted to the

implanted receiver stimulator through the skin via an inductive link by a wearable external


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control unit (ECU). Implant recipients normally use a ring-mounted thumb switch connected to

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

stimulation to achieve walking function. This study was undertaken to evaluate the potential for

better coordinating the actions of the stimulator with remaining volitional movements through a

more natural command interface than the manual switch.

The objective of this study was to evaluate the feasibility of detecting the intent to take a

step using the surface electromyogram (EMG) in an implant recipient with iSCI and eliminate

the need for manual triggering during FES-assisted ambulation. Our long-term goal is to specify

a new command and control interface that can be implemented with two channels of implanted

EMG recording electrodes with

adutta dissertation

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