Electromyographic pattern analysis and classiﬁcation for a...

Electromyographic pattern analysis andclassification for a robotic prosthetic arm

doi:10.1533/abbi.2005.0039

M. Jose H. Erazo Macias1 and S. Alejandro Vega2

1Department of Electric and Electronic Engineering, Technological Institute of Reynosa, Reynosa Tamaulipas, Mexico2Technological Institute of Superior Studies of Monterrey, Campus Queretaro, Queretaro, Mexico

Abstract: This paper deals with the statistical analysis and pattern classification of electromyographicsignals from the biceps of a person with amputation below the humerus. Such signals collected from anamputation simulator are synergistically generated to produce discrete elbow movements. The purposeof this study is to utilise these signals to control an electrically driven prosthetic or orthotic elbowwith minimum extra mental effort on the part of the subject. The results show very good separabilityof classes of movements when a learning pattern classification scheme is used, and a superpositionof any composite motion to the three basic primitive motions—humeral rotation in and out, flexionand extension, and pronation and supination. Since no synergy was detected for the wrist movement,different inputs have to be provided for a grip. In addition, the method described is not limited by thelocation of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained fromthe shoulder muscles. However, the presentation in this paper is limited to biceps signal classificationonly.

Key words: Electromyographic, amputated, orthotic, pronation, supination.

INTRODUCTION

Since World War II, many attempts have been made to useexternal power to operate artificial prosthetic or orthoticarms (Alderson 1954). Electromyographic (EMG) signalsfrom the body’s intact musculature have been suggestedand utilised by many researchers as an effective noninvasivemethod to provide commands to control an electricallypowered artificial limb (Graupe et al. 1978; Lyman et al.1974; Saidis and Stephenou 1977). Most of this researchhas produced single motion controls of the bang-bang type,which require special training and effort on the part of thesubject.

The introduction of microcomputers has made it pos-sible to experiment with more sophisticated signal detec-tion and motion control of human prostheses. Graupe andSalahi (1978) and Graupe et al. (1978) proposed and suc-cessfully implemented a time series identification to re-cover control information from synergistic EMG signals

Corresponding Author:M. Jose H. Erazo MaciasDepartment of Electric and Electronic EngineeringTechnological Institute of ReynosaReynosa TamaulipasMexicoEmail: erazo [email protected]

produced by the subject’s intact musculature. However,this project was terminated before any conclusive designprocedures were developed.

This paper presents an approach to detect, analyse, andclassify synergistic EMG signals generated by the biceps ofan above-the-elbow amputee in an attempt to move the ar-tificial limb without extra mental effort. The method is byno means limited to this location of the electrodes (Graupeand Salahi 1979). However, since stronger synergies areexpected from the arm muscles, they were prepared in thisstudy. An elbow of three degrees of freedom is considered.It was built using a newly designed product (made in theDepartment of Electric and Electronic Engineering, Tech-nological Institute of Reynosa, Tamaulipas, Mexico), whilethe mechanism and all its parts were made in the Techno-logical Institute of Reynosa (Figure 1). The elbow could bedriven to perform all possible simultaneous combinationsin the following six primitive motions:

• Humeral rotation inside• Humeral rotation outside• Elbow flexion• Elbow extension• Elbow pronation of wrist• Elbow supination of wrist

The wrist and hand grasp was not included in the finalclassification because no synergistic signals were obtained

C© Woodhead Publishing Ltd 113 ABBI 2006 Vol. 3 No. 2 pp. 113–119

M. Jose H. Erazo Macias and S. Alejandro Vega

Figure 1 Prosthetic elbow.

experimentally on the biceps. The grasp could be generatedthrough other muscles of the body.

A complete statistical analysis of the EMG signals corre-sponding to the above motions was first reported by Saridisand Newman (1978), who used simulated amputation mea-surements as described in the next section. The results in-dicated that the generated signals—owing to their nonsta-tionary properties—are unreliable for time series analysisand physiological interpretation, but their true averagesmay serve as feature vectors for pattern classification.

Subsequently, statistical pattern recognition algorithmswere used to classify the signals to classes correspondingto each of the possible signal and combined primitive mo-tions of the arm, and their misclassification error was stud-ied. Loading and velocity conditions found to be linearlyrelated to the latter’s relations to the features associatedwith the classification problem, for example, zero crossing(ZC) and signal variance, were established (Bigland andLippold 1954). Using envelopes of the classes for allloads and velocities, a classification algorithm independentof varying loading and velocity conditions was obtained.Finally, by discovering certain linear superposition prop-erties on the statistics of the features, it was possible todecompose combined motion to their relative velocities.

Load information was obtained through the integral ofthe muscular activity variable evaluated from the ZC andvariance measurements. The generation of the control al-gorithms, as well as further elaboration on the muscular ac-tivity variable, will be reported in a follow-up paper aimedat the direct control of a prosthetic arm by EMG signalswith minimum effort or training on the part of the amputee.

EXPERIMENTAL PROCEDURE

The EMG signals are easily gathered using skin electrodes.In this study, two electrode sites were used. At each site,one differential silver–silver chloride electrode is separatedby a ground electrode. They are separated from the skinby gel-impregnated foam, and were attached by an adhe-sive foam pad. The electrodes are placed parallel to thehumeral axis, slightly off the center of the bulge of each ofthe biceps. The locations are the lowestmost placementsthat are undamaged in most above-the-elbow amputees forwhom this work will be applicable (Newman and Saridis1978; Rimroth et al. 1978; Saridis and Newman 1979; Wirta

Figure 2 Electrode. Test on real muscle.

et al. 1968). An on-board microcomputer should sample thetwo signals and sequentially update its decision as to whichmotion is meant to be in progress. The stream of decisionsis fed into a coordinator of the motions of the arm. Thisis done at intervals of several milliseconds. The amputee’svisual feedback of the arm’s position and velocity shouldthus provide accurate enough correction of the motion.We note that the coordinator is designed with a mem-ory to provide trend characteristics of the decision streamto eliminate inadvertent errors and improve classificationaccuracy.

Experimental data were obtained from an amputationsimulator instead of a real amputee, where the subjectwould completely immobilise his arm in a cast at and belowthe humerus. The device electrode is shown in Figure 2(Saridis and Newman 1979). The reason for the simulationwas, first, availability of more subjects from a student pop-ulation, for example, three different subjects have alreadybeen used, and second, the flexibility to use the results fordesigning either upper limb prostheses or orthoses. Theresults are claimed to be representative of EMG signalsfrom actual amputees with nonatrophised musculature be-cause with the cast, the subject would generate the samesynergistic signals over his biceps when he would attemptto move his immobilised arm.

The experiments were performed with the arm in avertical position. Small-angle variations of the arm positionaround the perpendicular tested showed no effect in theclassification. Over 30◦ angle changes in the position of thearm from the vertical, as well as a test on real amputees,are planned in a later phase of this project.

The amputation simulator is constructed of cast alu-minum and extends from just proximal to the elbow to thefingertips. The cast holds the elbow at a mid-elbow flex-ion of 90◦, humerus to forearm, and pronation–supination.

114ABBI 2006 Vol. 3 No. 2 doi:10.1533/abbi.2005.0039 C© Woodhead Publishing Ltd

EMG pattern analysis and classification for a robotic prosthetic arm

Differential Amplifier

Vs

GND

Input

Input

R9

R8R7

R6

R5

R4

R3

R2

R1

Low-pass filter

Input X

Output m

R18 R19

R20

R21

C6

C5C4

C3

R17

R16

R15R14

High-pass filter

InputOutput

C2C1

R13R12

R11R10

−+

−+

−+

−+

−+

−+

Figure 3 EMG amplifier and filters.

When the shoulder has normal freedom of motion, its posi-tion may be monitored through potentiometers mounted ateach pin joint. The aluminium elbow mounting bracket forthe two high-gain, high input impedance EMG amplifiersis shown in Figure 3.

Twenty-nine motions were defined, along with onecontrol (zero) motion, each consisting of either graspopen/close, or one to three of the wrist, elbow andhumeral primitives. A more detailed description is given inNewman and Saridis (1978) and Saridis and Newman(1979). Since the biceps waveforms apparently containedvery little information from the grasp primitives, classifica-tion of the grasp primitives was not pursued further in thisstudy.

Figure 4 Outline of the arm with the weight, showing thevectors that intervene in the taking of the signals.

STATISTICAL EMG ANALYSIS

These signals will be registered in a team cyberamp 100,with gain (100), for conditioning of the signals, withjoining of AC of 0.01 Hz using another cyberamp 380,with gain (50), with joining AC of 10 Hz. After filtra-tion it passes the first floor of 400 Hz. We also have afilter that eliminates the noise generated in the band of60 Hz.

A card digidata 1322A with 16 resolution bites occu-pies a frequency of sampling of 2000 per second. At firsta weight is placed in the palm of a person for 5 s in aseated position, and this is repeated with different weights(Figure 4). The weights used are as follows:

Weights

P1 240 gP2 610 gP3 650 gP4 940 gP5 1.74 kgP6 2.98 kgP7 3.24 kg

Sequences of EMG data were obtained with a student ofnormal constitution, 27 years old, making two series, onein the morning and the other in the afternoon.

Morning

Name of the file: Sitting position Event

EMG19 0000 P5+P6+P7EMG19 0001 P5EMG19 0002 P5+P1EMG19 0003 P5+P1+P2EMG19 0004 P5+P1+P2+P3EMG19 0005 P5+P1+P2+P3+P4EMG19 0006 P5+P6EMG19 0007 P5+P7

115C© Woodhead Publishing Ltd doi:10.1533/abbi.2005.0039 ABBI 2006 Vol. 3 No. 2


Name of the file: Foot position Event

EMG19P 0000 P5+P6+P7EMG19P 0001 P5EMG19P 0002 P5+P1EMG19P 0003 P5+P1+P2EMG19P 0004 P5+P1+P2+P3EMG19P 0005 P5+P1+P2+P3+P4EMG19P 0006 P5+P6EMG19P 0007 P5+P7

Afternoon

Name of the file: Sitting position Event

EMG19V 0000 P5+P6+P7EMG19V 0001 P5EMG19V 0002 P5+P1EMG19V 0003 P5+P1+P2EMG19V 0004 P5+P1+P2+P3EMG19V 0005 P5+P1+P2+P3+P4EMG19V 0006 P5+P6EMG19V 0007 P5+P7

Name of the file: Foot position Event

EMG19VP 0000 P5+P6+P7EMG19VP 0001 P5EMG19VP 0002 P5+P1EMG19VP 0003 P5+P1+P2EMG19VP 0004 P5+P1+P2+P3EMG19VP 0005 P5+P1+P2+P3+P4EMG19VP 0006 P5+P6EMG19VP 0007 P5+P7

These registrations were introduced into a program, tobe run in the MatLab software, to obtain the correspondinggraphs for each one of the cases, some of which are shownhere.

Once the data are registered, MatLab provides the meanvalues and the securities RMS or the combinations that arewanted.

The mean of the signal, composed of a high number of(more than 500) sample points, is verified by computationin MatLab (Figure 5). The variance σ 2 of the EMG signalv(t) as given by

σ 2 = 1800

800∑t=1

v(t)2

It is used as one variable. The absolute value of the moment|σ 3| given by

|σ 3| =∣∣∣∣∣ 1800

800∑t=1

v(t)3

∣∣∣∣∣is another variable. The absolute value is taken to greatlyreduce within-class separation. Also computed is the fourth

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Signal’s half of absolute value

Signal’s half of absolute value

0.2

0.15

0.1

0.05

0

0.1

0

0.02

0.04

0.08

0.06

Sig

nal’s

Var

ianc

e

EMG Signal

Figure 5 Electromyograph of the variance against thestocking of the absolute value of the signal.

moment σ 4, the absolute value of the fifth moment |σ 5|,and the number of times the signal potential passes throughor touches zero (i.e., ZCs).

Also computed for each signal were the cross correla-tions between the bicep signal Rb(τ ), the power spectraSb(ω), computed by fast Fourier transform, and the signalvoltage density Db(v). Density and correlation were laterdiscarded because they did not apparently contribute classseparation, that is, in distinguishing between motions.

Sb(ω) and Rb(τ ) have both 800 components, each ofwhich is a measurement variable. Although the informationcontent of an autocorrelation and a power spectrum is in asense redundant, and although R(0) = σ 2, all of these arecomputed to simplify the process of feature selection. Themeasurement space, therefore, has 2058 components.

[σ 2 + |σ 3| + σ 4 + |σ 5| + ZC + R(τ ) + S(ω)].

The final conclusions from the statistical analysis are asfollows.

• The EMG signals are not stationary and are band limitedto virtually 1200 Hz.

• No synergistic signals are generated from the hand grasp.• Pattern information of the arm is contained in the ZC and

depends on the electrical activity of the muscles.• Loading and velocity information of the arm is contained

in the ZCs and variance, and depends on the internal mus-cular electrical activity.

• Motion information is concentrated mostly in the variancesand less in the higher-order moments.

• In most motions, the variance and ZC information aregrouped in separable clusters that may be approximatedby Gaussian densities, and their moments represent theclasses.



Trajectory Segmentation Tasks

Subtasks

Motion Blender

Learning Automaton

Subgoal1

Subgoalp

Controller1

Controllerp

Subprocess1

Subprocessp

EMG Command

ElementaryMotions

Level 1Organization

Level 2Coordination

Level 3Control

ProcessArm

Figure 6 Hierarchically intelligent control of a prostheticarm.

All of the above indicate that pattern recognition methodsmay be applicable to classify and recognize the variousmotions under consideration.

PATTERN CLASSIFICATION FOR EMG SIGNALS

Hierarchical intelligent control methods (Saridis andStephenou 1977) may be applicable to drive the pros-thetic arm if movement and loading information is prop-erty decoded and applied to a microcomputer system, pro-grammed accordingly. Pattern recognition methods arequite adequate to provide such information as will be ob-vious from this study. The final control configuration ispresented in Figure 6 for an arm incremental motion.

In most pattern recognition problems, it is necessary togreatly reduce the measurement space into a feature spaceof suitable dimension so that the final pattern recognitionalgorithm can be managed using an on-board microcom-puter. This will be accomplished at the end of this study byselecting as features the variables that contain maximuminformation on class separation.

The variance of the signal was selected as the measureof scatter to determine the importance of each variable. Ifa variable has large between-class and small within-classscatter, that is, the criterion for class separation, for anytwo classes, then it is considered important. All unimpor-tant variables are immediately discarded. The ranking ofeach of the remaining features is determined by the totalnumber of classes separated and the relative amount ofseparation of the variable. When the feature count can be

reduced to 50 or less, a computer can handle the com-putations necessary for a rigorous mathematical analysisto further minimize the feature vector, while consider-ing any resulting classification error that may be expected(Fukunaga 1972). The learning algorithm (Appendix I)makes it possible to determine the number of iterations foreach pair of movements in the elbow.

THE PROPERTY OF SUPERPOSITION OF PRIMITIVES

Owing to the physical nature of the problem, it seemsreasonable, perhaps even more straightforward, to con-sider only six classes, each defined as the presence of oneof the primitives. In doing so, it was noted that usingthe variances, the primitives exhibit superposition proper-ties, which might provide the means to develop a six-classscheme. That is, linear combinations of the variance dataare taken for more than one primitive, the resulting distri-bution approximates that of the combined motion involv-ing the primitives used. Thus, distributions of combinedmotions should be decomposed into the distributions oftheir primitives, possibly permitting primitive identifica-tion from the variances of unknown motions.

For each pair of primitive motions, coefficients of alinear combination of points from the two variance distri-butions were calculated, which resulted in the mean of thederived distribution being equal to the mean of the actualcombined motion’s distribution (see Appendix II).

CONCLUSIONS

Considerable data are available regarding communicat-ing information from a below-the-humerus incapacitatedsubject and a microcomputer system programmed to hi-erarchically implement intelligent control of a prostheticarm.

Learning pattern classification methods and primitivedecomposition may yield fairly accurate commands on gen-erating effects on this subject for a variety of loading andvelocity conditions. Memory of the previous movementand visual feedback may be utilised to interrupt a motionthat was incorrectly understood. The features needed areonly ZCs and variances, as in simple speech recognitionproblems (Rabiner and Schafer 1978). The integrated mus-cular activity may be used to evaluate the load and ve-locity parameters involved. Further research is aimed atexperimental verification of the actual movement and con-trol of the prosthetic arm. The final test of the methodwill be with actual amputees from the local rehabilitationcenter.

REFERENCES

Alderson S. 1954. The electric arm. In Klopsteg PE, Wilson PD,eds. Human Limbs and Their Substitutes. New York:McGraw-Hill, Chap. 13. (reprinted by Hafner, 1969).



Bigland B, Lippold OCJ. 1954. The relation between force, velocityand integrated electrical activity in human muscles. J Physiol,123:214–24.

Fukunaga K. 1972. Introduction to Statistical Pattern Recognition.New York: Academic Press.

Graupe D, Salahi J. 1979. Multifunctional Artificial Limb Controlvia EMG Temporal Analysis—Background and AmputeeTests [PhD thesis]. Department of Electrical Engineering,Illinois Institute of Technology, Chicago.

Graupe D, Magnussen J, Bees A. 1978. Microprocessor system formultifunctional control of upper-limb prostheses. IEEE TransAutomat Contr, AC-23:538–44.

Lyman J, Freedy A, Zadaca H. 1974. Studies and development ofheuristic end-point control for artificial upper limbs. UCLABiotechnology Laboratory. Technical report 54.

Newman MA, Saridis GN. 1978. Development of prosthetic arm.Purdue University, West Lafayette, IN. AARL Memo 30.

Rabiner LR, Schafer RN. 1978. Digital Processing of SpeechSignals. Englewood Cliffs, NJ: Prentice-Hall.

Rimroth P, Newman MA, Saridis GN. 1978. A study ofelectromyography for above-elbow amputation. PurdueUniversity, West Lafayette, IN. AARL Memo 34.

Saridis GN, Newman MA. 1979. Upper limb EMG statisticalanalysis. In Proceedings of MIDCON’79, Chicago, IL.

Saridis GN, Stephenou HE. 1977. A hierarchical approach to thecontrol of a prosthetic arm. IEEE Trans Syst Man Cybernet,SMC-7:407–20.

Wirta RW, Taylor DR, Finley FR. 1968. Engineering principles inthe control of external power by myoelectric signals. Arch PhysMed, 49:294–6.



APPENDIX I: THE STOCHASTIC APPROXIMATIONALGORITHM FOR COMPUTATION OF DISCRIMINANTCOEFFICIENTS

The general linear-discriminant classifier for two classesω1 and ω2, V can be written as

h = VT X + v0><

0 ⇒ X ∈{

ω1ω2

. (AI.1)

This can be rewritten as

h = WTY ><

0 (AI.2)

h =n∑

i=0

wi yi , (AI.3)

where y0 ≡ 1, y1 = x1, that is, w0 = v0.Now, if we let Z = Y for Y∈ ω1, and Z = −Y for Y ∈

ω2, then WT Z > 0 for correct classifications.The stochastic approximation can be expressed as

Wl+1 = Wl − al[WT

l Z1 − γ (Zl )]

Zl , (AI.4)

where γ (Z1) is the desired classifier output (>0) for Z1,and a1 is required to satisfy

liml→∞

a1 = 0,∞∑

l=1

a1 = ∞, and∞∑

l=1

a2/ < ∞

To guarantee convergence, for example, a1 = 1/l. In orderto speed up the convergence, the number a1 = 1/k wasadvanced to 1/k + 1 only when the term

[WT

l Zl − γ (Z1)]

changed sign.

APPENDIX II: SUPERPOSITION OF PRIMITIVES TOMATCH THE MEAN OF MULTIPLE PRIMITIVE MOTIONS

Let the mean of the double-primitive motion being con-sidered be[

σ 2B

σ 2T

].

Let the data of the component primitives be[σ 2

Bik

σ 2Tik

]and

[σBjk

σ 2T jk

], i = 1, 27, j = 1, 20.

The data are used with the equation[σ 2

Bnew

σ 2Tnew

]= c1 ·

[σ 2

Bik

σ 2Tik

]+ c2 ·

[σ 2

Bjk

σ 2T jk

]

with coefficients c1 and c2 such that the mean of the(σ 2

Bnew, σ 2Tnew

)distribution is equal to[

σ 2B

σ 2T

].

c1 and c2 are easily found by

c1σ2Bi + c2σ

2T j = 2σ 2

B. (AII.1)

And

c1σ2Ti + c2σ

2T j = 2σ 2

T . (AII.2)

The solution of (AII.1) and (AII.2) is

c2 =(2σ 2

T − 2σ 2Bσ 2

Ti /σ2Bi

)(σ 2

T j − σ 2Bj σ

2Ti /σ

2Bi

) (AII.3)

c1 =(2σ 2

B − c2σ2Bi

)σ 2

Bi. (AII.4)


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