Intention Detection Usinga Neuro-Fuzzy EMG Classifier

Increasing the Potential of the Gabor Matching Pursuit Algorithmto Help Detect the Intent of a Person to Stand Up or to Sit Down

One of the most important factors inprosthetic and orthotic controllers is

the ability to detect the intention of theperson to perform a certain activity suchas standing up, quiet standing, walking,and sitting down. For these applications,detecting the intention of the person toperform an activity relieves them from theburden of conscious effort in operating thesystem. Electromyography (EMG) hasbeen used extensively for intention detec-tion and can be considered a bandlimitedstochastic process with Gaussian distribu-tion and zero mean, which has varyingspectral characteristics in time [1], [2].

Various EMG features have been usedfor intention detection including the num-ber of zero crossings [3], the EMG fre-quency characteristics [4], and the meanabsolute values [5]. There are a number ofdrawbacks that have been associated withthese methods such as the high electrodesensitivity to electrode displacement, lowrecognition rate, and a perceivable delayin control [1].

In this article we discuss a techniquefor EMG applications that decreasesglobal delay time and improves time spec-tral analysis. The technique is aimed atimproving the Gabor matching pursuit(GMP) algorithm through the use of ge-netic algorithms. The key stage of this de-sign feeds EMG features to a neuro-fuzzyclassifier that can be designed to detect theintention of the patient.

OverviewHudgins et al. [3] investigated the

problem of control of a multifunctionprosthetic limb. They applied electrodesconsisting of a single differential channelwith an active electrode over each of thebiceps and triceps brachii muscles. Theythen selected a number of features, whichincluded mean absolute values, mean ab-solute values slope, number of zero cross-

ings, slope sign changes, and wavelengthor wave complexity. These features werefed to a three-layer feedforward neuralnetwork. Chan et al. [1] used a similar ap-proach and showed improved resultswhen using a fuzzy classifier.

Recently, wavelet analysis has beenused as a technique for EMG estimation andclassification. For EMG estimation,Karlsson et al. [2] used two approaches; onebased on the fast Fourier transform (FFT)and the other on wavelet packets (WPs).They investigated the performance of thetwo approaches both on computer-synthe-sized EMG signals and on real EMG signalsfrom healthy subjects during sustained iso-metric knee extension and compared theseapproaches to different traditional methodsfor signal estimation. They also introducedthe wavelet shrinkage method that reducedthe mean-square error of WP and FFT spec-tral estimates. EMG classification of uterinecontractions was demonstrated by Khalil etal. [6] and their technique based the classifi-cation on the comparison of the varianceco-variance matrices computed from signaldecomposition using both orthogonal andnonorthogonal wavelet bases.

Zhang et al. [7] used another type ofwavelet analysis technique to analyzesomatosensory evoked potentials for de-tecting cerebral injury in its earlier phases.They presented a model of rat focal cere-bral ischemia and used the discrete Gaborspectrogram to analyze somatosensoryevoked potential signals for earlynoninvasive detection of brain focalischemic injury. They displayed a time-varying spectrum of the somatosensoryevoked potential as a contour map on the

two-dimensional (2-D) time-frequencyplane from which the difference betweenischemic and normal regions can be seen.Durka et al. [8] collected evidence sug-gesting that matching pursuit (MP) is agood candidate for a universal high-reso-lution parameterization of EEG data,compatible with the visual and spectralanalysis, and applicable to a large class ofproblems. They discussed the need for agenerally applicable method for a mathe-matical description (parameterization) ofthe signal, which would be directly relatedto the heritage of the traditional EEG anal-ysis. In this context, the authors discussedthe application of the MP algorithm andpresented recent advances in analysis ofsleep EEGs. The authors also addressedsome of the drawbacks associated withgreedy MP including the relative require-ment for large computational resources.

We propose a novel technique forEMG applications that we hypothesizewill decrease significantly the global de-lay time and improve the time spectralanalysis. This method is proposed to im-prove the GMP technique introduced byMallat et al. [9] by using genetic algo-rithms to search for the best bases. Themethod was applied to detect the intentionof a paraplegic person to stand up or to sitdown for use with an electrical stimula-tion orthosis [10]. The best bases werefound using single-site EMG signals fromthe biceps and triceps brachii muscles dur-ing standing up and sitting downmaneuvers. The choice of these two mus-cles was due to their major involvement instanding and sitting activities as well asthe ease to access them. The bases couldthen be used to analyze EMG signals toextract time-invariant features. A finalstage of this design would be to feed thesefeatures to a neuro-fuzzy classifier, whichcould be designed to detect the intentionof the patient.

November/December 2002 IEEE ENGINEERING IN MEDICINE AND BIOLOGY 1230739-5175/02/$17.00©2002 British Crown Copyright

Sherif E. Hussein and Malcolm H. GranatBioengineering Unit,

University of Strathclyde, Glasgow

MethodsGabor Matching Pursuit

To obtain efficient representations offunctions, we seek to approximate func-tions with linear combinations of a smallnumber of unit vectors from a family gi ina Hilbert space H. In pattern recognitionapplications, the gi in the expansion of asignal f are interpreted as features of f.Compact expansions highlight the domi-nant features of f and allow f to be charac-terized by a few salient characteristics. Ifwe translate the signal slightly, then an ac-curate approximation can require manyelements. To avoid this, we need a class offunctions that are translation invariantfrom a dictionary of redundant bases. Theproblem of finding an appropriate dictio-nary is essentially one of finding a set ofcanonical features, which characterize thefunctions we wish to decompose. For a re-dundant dictionary, we may pay a highcomputational price to find an expansionwith M dictionary vectors that yields theminimum approximation error.

Qian and Chen [11] as well as Mallatand Zhong [9] developed a greedy algo-rithm called matching pursuit, which pro-gressively refines the signal approximationwith an iterative procedure. They also con-structed a time and frequency translationinvariant Gabor dictionary D, which is avery redundant set of functions by scaling,translating, and modulating a Gaussianwindow. Gaussian windows are used be-

cause of their optimal time and frequencyenergy concentration.

The matching pursuit method repre-sents a signal as

f t a g ti ii

( ) ( )==

∞

∑0

.(1)

For Gabor dictionary

g t b h t f ti i i i i( ) ( ) cos( )= ⋅ +2π ϕ (2)

h t eit p si i( ) (( ) / )= − −π 2

(3)

where bi are the normalizing factors tokeep the norm of g ti( ) equal to one, fi andϕ i are the frequencies and phases for thecosine functions, si control the envelopewidths, and pi control the temporal place-ments. Equation (2) is written in a discreteform for a signal f with N samples as:

g n b hn p

s

k n

Ni i

i

i

ii( ) cos= −

+

2π ϕ ,

0 ≤ <n N . (4)

The window scale si has to be in therange between 1 and N, and the fre-quency index ki and the time position pimust lie between 0 and N −1. In the algo-rithm developed by Mallat et al., thescale had been chosen limited to an ex-ponential relation with a dilation factorα that equals 2 such that s j= α , in orderto reduce the computation, where j is theoctave of the scale s, which varies be-tween 0 and logα N . Consequently, a setof parameters λ ϕi i i i i ib p s k= ( , , , , ) has tobe found for each atom.

If g t0( ) is one of the atoms in the dictio-nary, the first projection decomposes thesignal into

f f g g R f= +, 0 01 . (5)

Since R f1 is orthogonal to g0, the total en-ergy of the signal f is

f f g R f2

02 1 2

= +, .(6)

Repeating i times for the residue

R f R f g g R fi ii i

i= + +, 1 .(7)

The orthogonality of R fi+1 and gi implies

R f R f g R fi ii

i2 21= + +, .

(8)

To minimize R fi+1 we must choose

g Di ∈ such that| , |< >R f gii is maximum.

Summing from i between 0 and M −1yields

f R f g g R fii

i

M

iM= +

=

−

∑ ,0

1

.(9)

Numerical experiments show that thenorm of the residues decreases quickly forthe first few iterations, but afterwards thedecay rate slows down and remains ap-proximately constant [12]. When we usethe Gabor dictionary, the coherent struc-tures of a signal are those portions of a sig-nal that are well localized in the time-frequency plane. White noise is not effi-ciently represented in this dictionary be-

124 IEEE ENGINEERING IN MEDICINE AND BIOLOGY November/December 2002

Per

cent

age

Err

or

Number of Atoms

100

90

80

70

60

50

40

30

20

10

00 10 20 30 40 50 60 70 80

1. The percentage error curves for the number of atoms found using the modifiedGabor matching pursuit for both standing up (shown by the solid curve) and sittingdown (shown by the dashed curve).

A tradeoff must be

done to achieve a

reasonable delay time

and a very low

probability of false

alarms.

cause its energy is spread uniformly overthe entire dictionary. A residue, which isconsidered dictionary noise with respectto one dictionary, may contain many co-herent structures with respect to anotherdictionary; for many signal-processingapplications, the dictionary defines a setof structures that we wish to isolate. Ex-pansions to coherent structures allow us tocompress much of the signal energy into afew elements.

The matching pursuit behaves like anonlinear chaotic map, and for particulardictionaries the normalized residues con-verge to an attractor. This attractor is a setof signals that does not correlate well withany g D0 ∈ because the coherent structureof f in D is removed by the pursuit. For sig-nals in the attractors, this correlation has asmall amplitude that remains nearly equalto a constantC D, which depends on the dic-tionary D. Such signals do not correlatewell with any dictionary vector and arethus considered noise with respect to D.

Feature Extraction

Genetic AlgorithmsThe GA method can be used with the

GMP algorithm to reduce the computa-tions. GAs have been proposed in this re-search to calculate the parameter vector γ iaccompanied by each atom. The proce-dure starts, for each iteration i, byinitializing a population of chromosomesusing binary representation. Each chro-mosome represents a possible solution foran atom gi. A number of evolutionallyevolutionary operators including selec-tion, crossover, and mutation are then ap-plied to reproduce a newer generationwith a pool of possible solutions relativelybetter than the older ones [13]. The algo-rithm uses tournament selection and aimsto decrease the normalized root meansquare error (NRMSE) between the origi-nal signal and the reconstructed signal

NRMSE ij

N

j

N

e j

f j

= =

−

=

−

∑

∑

2

0

1

2

0

1

( )

( )(10)

where e j( ) is the difference between theoriginal and the reconstructed signal andN is the number of samples for the signal f,which has a power of two number to re-duce the computations.

The process is repeated for each addi-tional Gabor atom until NRMSE reaches apredefined value.

Window Length SelectionThe selection of the number of samples

N used in processing is very important inthe detection process. Large N decreasesthe probability of detecting short events

but increases the detection delay. On theother hand, small N increases the proba-bility of false alarms but decreases the de-tection delay.

November/December 2002 IEEE ENGINEERING IN MEDICINE AND BIOLOGY 125

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plitu

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1

0.8

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

−10 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Freq

uenc

y (H

z)

250

200

150

100

50

0

(a)

Time (s)

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

(b)

Freq

uenc

y (H

z)

250

200

150

100

50

0

Time (s)

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

(c)

2. (a) The actual EMG signal shown by the dotted curve and its approximation usingthe modified Gabor matching pursuit shown by the solid curve for standing up (b),(c) are the equivalent Wigner-Ville distribution for the actual EMG signal and theGabor functions selected by the modified matching pursuit, respectively.

A tradeoff must be done to achieve areasonable delay time and a very lowprobability of false alarms. In the pro-posed algorithm, N is selected as powersof two to decrease the chromosomelength.

Features SelectionGabor matching pursuit searches for

atoms that represent the coherent struc-ture of the signal. These atoms are spreadover a number of scales that can only beinside the range of [ , log ]0 2 N and are

found using GAs. For classification pur-poses, the signal is projected over the at-oms found and the resulting coefficientsare used to calculate the energy content ofeach predetermined scale. A further stepof normalizing the energy content of eachscale relative to the total energy of the sig-nal, which has N samples, is then per-formed to produce the necessary features.

Neuro-Fuzzy ClassificationThe classifier for EMG signals to de-

tect standing up and sitting down was de-signed using an adaptive neuro-fuzzyinference system (ANFIS), which can befound in the MATLAB Fuzzy LogicToolbox. This uses data collected after atrigger and for 512 ms from electrodesconsisting of a single differential channelwith an active electrode over each of thebiceps brachii and triceps muscles duringstanding up and sitting down with a sup-porting frame.

ANFIS is functionally based on theSurgeno-type fuzzy rule base and at thesame time has an architecture equivalentunder some constraints [14] to a radial ba-sis function neural network, allowing thesystem to learn from the training data.

The design started by subtractive clus-tering to determine the number of rulesand the input membership functions. Themembership function of choice was thegeneralized bell function:

µ( )xx c

a

b=

+ −

1

12

.

(11)

Subtractive clustering is an unsuper-vised algorithm based on a measure of thedensity of the normalized data points inthe feature space. The point with the high-est number of neighbors is selected as thecenter for a cluster. The data points withina prespecified fuzzy radius are then re-moved and the algorithm looks for a newpoint with the highest number of neigh-bors until all the data points are checked.

The following two rules were a part ofthe first-order Surgeno-type rule base:

If u1 is A1 and u2 is B1 then

y c u c u c1 11 1 12 2 10= + + (12)

If u1 is A2 and u2 is B2 then

y c u c u c2 21 1 22 2 20= + + . (13)

The fuzzy classifier can interpolate be-tween the two linear rules depending ontheir state. So, if the firing strengths of therules are α1and α2 for two inputsu1 andu2,

126 IEEE ENGINEERING IN MEDICINE AND BIOLOGY November/December 2002

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0

−0.2

−0.4

−0.6

−0.8

−10 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Freq

uenc

y (H

z)

250

200

150

100

50

0

(a)

Time (s)

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

(b)

Freq

uenc

y (H

z)

250

200

150

100

50

0

Time (s)

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

(c)

3. (a) The actual EMG signal shown by the dotted curve and its approximation usingthe modified Gabor matching pursuit shown by the solid curve for sitting down; (b)and (c) are the equivalent Wigner-Ville distribution for the actual EMG signal andthe Gabor functions selected by the modified matching pursuit, respectively.

respectively, then the output based onweighted average is

y y y= + +( / ( ))α α α α1 1 2 2 1 2 (14)

= += + +

+ + +

β ββ

β

1 1 2 2

1 11 1 12 2 10

2 21 1 22 2 20

y yc u c u c

c u c u c

( )

( )= + +

+ + +β β β

β β β1 11 1 1 12 2 1 10

2 21 1 2 22 2 2 20

c u c u c

c u c u c .(15)

Using the least-squares method, cij ,( ,i = 1 2 and j = 0 1 2, , ) could be adjusted inthe forward pass while the membershipfunction’s parameters ai, bi, and ci couldbe adjusted by gradient descent using theerror signals that propagate in the back-ward pass [14].

Both the features extractor and theneuro-fuzzy classifier have been imple-mented using MATLAB and then inte-grated into a LabVIEW program forreal-time testing.

ResultsEMG signals from a paraplegic sub-

ject, with a complete lesion at the level ofT6 and minimal lower limb spasticity,were recorded while the subject stood upand sat down with the help of a frame forsupport. These signals were recorded at 1kHz. For finding the coherent structure ofthe EMG signal, overlapping segments,with lengths of 512 samples each, wereprocessed using the modified GMP. Thegenetic operators used were selected touse a uniform crossover of 0.6 and a uni-form mutation of 0.1. The chromosomelength was selected such that 9 bits repre-sented each of si, pi, and ki, while ϕ i wasrepresented by 13 bits, which made a totallength of 40 bits. Each generation had apopulation size of 20 chromosomesevolved for at least 40 generations beforeit proceeded to search for the next Gaboratom. The criteria used for selecting a newGabor atom was that the algorithm wouldcontinue searching until the new Gaboratom did not reduce the NRMSE

Figure 1 shows the relation betweenthe percentage errors and the number ofatoms found using the GMP for bothstanding up and sitting down. It is clearthat there is initial rapid drop of the per-centage error followed by a smallerchange.

Figures 2(a) and 3(a) show two EMGsignals during standing up and sittingdown, respectively, together with the esti-mated two EMG signals found by themodified GMP. To demonstrate the per-

formance of the estimation technique,Figure 2(b) and (c) shows theWigner-Ville Distribution (WVD) fortheir actual signals and for the bases foundusing modified GMP during standing upwhile Figures 3(b) and (c) shows WVDduring sitting down. Both Figures 2 and 3show no significant difference betweenthe spectrograms for the actual and the ap-proximated signals.

A total of 30 EMG signals during stand-ing up and another 30 EMG signals duringsitting down were collected, and consecu-tive overlapping segments were processedindividually to find the Gabor atoms foreach of the EMG signals. These atomswere then sorted globally in a descendingorder according to the absolute coefficientsof the bases found using the modifiedGMP. The next stage was selecting the best

November/December 2002 IEEE ENGINEERING IN MEDICINE AND BIOLOGY 127

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0.4

0.2

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−0.4

−0.6

−0.8

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5. The actual EMG signal shown by the dotted curve and its approximation shownby the solid curve using the selected bases from the modified Gabor matching pur-suit for sitting down.

Per

cent

age

Err

or

Number of Atoms

100

90

80

70

60

50

40

30

20

10

00 10 20 30 40 50 60 70 80 90 100

4. The percentage error curves for the selected bases found using the modifiedGabor matching pursuit for both standing up (shown by the solid curve) and sittingdown (shown by the dashed curve).

100 bases. Four scales (scales 1 to 4) werefound from the selected bases and thus thebases were divided into four bands accord-ing to which scale they belong to, and eachband had energy content normalizedagainst the total energy. The EMG signalswere then used to extract the learning fea-tures for both standing and sitting.

The relation between the percentageerrors for two of the EMG signals and thenumber of the selected bases are shown inFigure 4, which shows a steep drop in theerrors for signals that were not used intraining. Figure 5 shows an actual and ap-proximate sitting-down EMG signal con-structed using the coefficients found fromthe selected bases.

A neuro-fuzzy classifier was then de-signed using these sets of features to de-tect the paraplegic person’s intention tostand up or to sit down, which were set to 1and −1 respectively. Seven generalizedbell membership functions for each of thefour inputs as well as a rule base of 30rules were found with an average learningerror of 0.126 for the designed classifierusing ANFIS. Positive false classifica-tion, detection of an intention that doesn’texist, is a major problem that can happen,and this is more serious than a negativefalse classification in which a subject in-tention is not detected. The thresholds of−0.7 and 0.7 were therefore selected suchthat the output of the classifier had to ex-ceed 0.7 in order to indicate the intentionto stand up and to be less than −0.7 for in-dicating the intention to sit down to de-crease possible posi t ive falseclassification. This classifier was testedwith two different paraplegic subjects;one with an incomplete lesion at T7 andthe other with a complete lesion at T8.There was no positive false classificationfrom either subject (Table 1).

DiscussionEMG signals have been used exten-

sively for intention detection, but the tech-niques assumed local stationary of theEMG. The GMP on the other hand de-scribes the signal structures in terms oftime-frequency parameters and is adap-tive to the local signal structure [9]. Thismakes the description of weak transientspossible and gives a high time-frequencyresolution. The algorithm estimation oftime-frequency energy distribution doesnot produce strong artifacts at coordinateswhere no activity occurs in the signal. Italso eliminates the components that do not

correlate well to the signal-coherent struc-ture [12].

Although many researchers considerthe GMP algorithm the best candidate foranalyzing nonstationary signals, the largecomputations associated with the algo-rithm limited some of the applicationsfrom using this robust method [8]. This ar-ticle proposed a way to search for the opti-mal Gabor atoms by iteratively selectingthem using GAs. The algorithm simpli-fied the complexity associated with theclassical method in which an iterative pro-cess was based on selecting the largest in-ner product of the signal with Gaborwavelet from a redundant dictionary. Themodification was to encode the parame-ters of each atom and to reduce theNRMSE associated with each atom. Theresults verify the performance of the pre-sented method in association with aneuro-fuzzy classifier to detect the inten-tion of a subject to stand up or to sit down.

In many applications a need for atranslation invariant method was neces-sary for classification and diagnosis pur-poses. GMP with its translation-invariantproperties offers an excellent method foranalyzing signals such as ECG, EEG, andEMG. Careful selection of the windowwidth for the sampled signal processedusing the matching pursuit basis is neces-sary, and in this research we used win-dows with overlapping intervals thatcould be controlled according to themaximum delay permitted for real-timeapplications. This technique avoids be-ing trapped into false alarms that occur inshort intervals, as this is dependent on thewindow width and not on the overlappinginterval. The combination between theexcellent properties of GMP and the de-crease in complexity and delay proposedby the modified GMP is a powerful tech-nique that, when combined with aneuro-fuzzy classifier as proposed in ourresearch, could remove the limitations ofthe GMP discussed by Durka et al. [8]and increase the potential benefits oftheir research.

This study also demonstrated the useof the modified GMP to detect the inten-tion of a paraplegic person to stand up orto sit down, which could be integratedwith an orthosis using electrical stimula-tion for controlling standing up and sittingdown maneuvers [15]. The applicationdealt with a two-case problem as this wasthe nature of the system under investiga-tion; however, the same technique could

128 IEEE ENGINEERING IN MEDICINE AND BIOLOGY November/December 2002

Table 1. Classification results from 30 tests for standing up and 30 testsfor sitting down. The number of tests are shown together with the out-

comes from these tests. Negative false classification refers to not detect-ing an intention that took place while positive false classification refers to

detecting an intention that did not take place.

Standing Up Sitting Down

Number of tests 30 30

Correct classification 29 28

Negative false classification 1 2

Positive false classification 0 0

Positive false

classification is a

major problem that is

more serious than a

negative false

classification in which

a subject intention is

not detected.

also be applied to other problems with amore complex structure.

Sherif E. Hussein re-ceived his B.Sc. andM.Sc. degrees (withhighest honors) in com-puter science fromMansoura University,Mansoura, Egypt, in1994 and 1997, respec-tively. He joined the ac-

ademic staff as a lecturer in the Computerand Systems Department at MansouraUniversity in 1997. He is now expected tofinish his Ph.D. in bioengineering fromthe Bioengineering Unit at University ofStrathclyde, Glasgow, UK. Sherif is amember of the Institute of Electrical andElectronics Engineers, the Institute ofElectrical Engineering, and the Interna-tional Organisation for Artificial Organs.His research interests include GAs, NNs,fuzzy logic, wavelet analysis, and theirapplications in biomedical signal and im-age processing.

Malcolm H. Granatjoined the Bioengineer-ing Unit at the Univer-sity of Strathclyde,Glasgow, in 1987 as aPh.D. student, where heis now a reader in bioen-gineering. Dr. Granathas been actively in-

volved in research in rehabilitation engi-neering, control of human movement, andinstrumentation for the last 12 years. Hehas 43 journal publications ranging fromstudies on the development of portablegait analysis instrumentation to the appli-cation and development of hybrid func-tional electrical stimulation systems forthe restoration of locomotion in spinalcord injury.

Address for Correspondence: MalcolmH. Granat, Bioengineering Unit, Univer-sity of Strathclyde, Glasgow G4 0NW,UK. Tel: +44 141 548 3032. Fax: +44 141

552 6098. E-mail: m.h.granat@strath.ac.uk.

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