Multiclass Motion Identification using Myoelectric

Multiclass Motion Identification using Myoelectric Signals and Support Vector Machines M. León, J.M. Gutiérrez, L. Leija, R. Muñoz

J.M. de la Cruz, M. Santos

Bioelectronics Section Department of Electrical Engineering CINVESTAV 07360 Mexico D.F., Mexico e-mail: [email protected]

Dpto. Arquitectura de Computadores y Automática Facultad de Fisica, Universidad Complutense de Madrid 28040 Madrid, Spain e-mail: [email protected]

Abstract— In this paper, different classifiers were trained to identify myoelectric registers, in order to recognize nine different motions related to four degrees of freedom of the forearm. Three main methods were compared, namely Linear Discriminant Analysis, Artificial Neural Networks and Support Vector Machines. The behavior of pattern recognition schemes was investigated using different amounts of data collected from 12 healthy subjects. The focus of this work is to identify the best classification scheme. Departure information was obtained using a preprocessing stage to extract either autoregressive or frequency domain features. Experiments show that the best performance is achieved employing frequency features and support vector machine classifier. This classification scheme demonstrates exceptional recognition accuracy of over the other methods. Keywords-component; Myoelectric signal; Support Vector Machine; Pattern recognition.

I.

INTRODUCTION

The Myoelectric Signal (MES), collected at the skin surface using electrodes, are capable of providing enough information about neuromuscular activity in a non-invasive manner. These measured records are an effective source of prosthetic control [1]. Most works related with signal classification are focused on improve the accuracy in the discrimination of movements. In this sense, first developed systems allowed to establish basic control functions from estimating the amplitude range or response time of MES [23]. Nowadays, computationally efficient algorithms have improved the signal processing stage in MES analysis. Among different reports related with MES classification, signal processing is often divided in three main stages: feature extraction, data reduction and pattern recognition methods. On the one hand, MES feature extraction can be accomplished using different techniques such as signal amplitude, autoregressive coefficients (AR) [4], frequency and power characteristics obtained of Fast Fourier Transform (FFT) analysis [5-6], integral of the absolute value [7-8], time and frequency histograms [9] and wavelet analysis [1012]. On the other hand, MES classification is performed using different techniques to obtain feasible identification systems. In this sense, it is possible to found applications using artificial intelligence techniques (i.e. artificial neural networks, genetic algorithms, fuzzy logic) [13-15] and statistical classifiers [16]. Recently, Support Vector Machine

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(SVM) has been shown to improve the performance in classification tasks. SVM is founded in the framework of the statistical learning theory, which is appropriate for approaching classification and regression problems [17-18]. SVM represent a new approach to pattern classification that has attracted a lot of attention in many real-world applications ranging from face identification and text categorization to bioinformatics and data mining [19-20]. The main advantage of SVM is related to their global and unique solution that allows them to not suffer from the problem of multiple local minima, such as artificial neural networks. In addition, the final complexity of these models does not depend on the dimensionality of the input space. Since they operate on the induction principle of structural risk minimization (which minimizes an upper bound on the generalization error) are less susceptible to the well-known problem of overfitting. There are some works describing the use of SVM either in medical diagnosis [21] or related to myoelectric control applied to robotic arms and upper limb prosthesis. Crawford et al. [22] reported the use of SVM for MES classification, in their work a set of seven single electrodes located in the forearm muscles were employed in order to distinguishing eight hand gestures, with the purpose of controlling a robotic arm with 4 degrees of freedom. Even though their classification rate was over 90%, only three subjects participated in the experiment. Similarly, Bitzer and Smagt have used SVMs to identify six finger actions from a set of signals obtained with ten active double differential electrodes located near to the wrist [23], the obtained classification rate was between 90-94% for relax and pronation arm postures. Another work developed by Lucas et al. [24], describe a multi-channel classification problem using eight electrodes where MES identification was performed to discriminate six movements of the hand using an approach based on SVM and wavelet coefficients. Final results show that the optimal wavelet selection favors the classifier performance, obtaining an average misclassification rate of 5%. Another report related with MES classification and SVM was presented by Naik el al. [25]. This paper reports a novel technique using Twin SVM (TSVM) [26] that allows to overcome the skew in class cardinalities. Here the multiclass problem is addressed using four active differential electrodes an applying Independent Component Analysis (ICA) to MES records, in order to recognize seven hand gestures with a classification performance of 86.02%. In all these works, it is

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possible to identify a low number of subjects in experimental sessions. This fact does not assure the robustness of the employed method. The aim of this work is to evaluate the feasibility of use SVM in MES classification problems and as myoelectric control tool of anthropomorphic arm in the future. To demonstrate this fact, others classifiers were constructed using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) to compare their performance. Classification accuracy was investigated using both autoregressive and frequency domain features. Furthermore, we compare the classification performance using different amounts of training and testing data. In this way, final efficiency and robustness models were also discussed. The paper is structured as follows. Section II describes the methodology of MES acquisition, as well as the protocol used with subjects to generate the different classification movements. Signal processing, feature extraction and classifiers are described in Section III. The experimental results are evaluated and discussed considering the three different schemes in Section IV. Finally, Section V gives conclusions and outlook on ongoing work. II.

BACKGROUND

(1)

Nonlinear classification problems are solved by mapping the original data into a feature space, in which the mapped data are linearly separable. Function Ԅሺ‫ڄ‬ሻ , which maps training vector ‫ݔ‬௜ into a higher dimensional space, requires belonging to dot product space. The dot product of mapping function is named as kernel:

k ( xi , x j ) = φ ( xi ) φ ( x j ) T

(2)

For a group of data that are mapped into the linearly separable space, the hyperplanes that divides the data into two labeled classes are shown as:

wT φ( x) + b = 0 w ∈ RN , b ∈ R

min w, b

m 1 2 w + C ¦ ξi 2 i =1

yi ( w φ( xi ) + b ) ≥ 1 − ξi , ξi ≥ 0, i = 1,..., m

(4)

T

where ߦ௜ is called the slack variable which takes different values for each ‫ݔ‬௜ and is related to the concept of soft margin, meanwhile ‫ ܥ‬is the tuning parameter used to balance the margin and tolerating errors in the training data. Equation (4), can be solved by introducing Lagrange multipliers ߙ௜ through next expression: m

max ¦ α i − α

i =1

1 m ¦ αi α j yi y j k ( xi , x j ) 2 i , j =1

m

(5)

¦ αi yi = 0, 0 ≤ αi ≤ C , ∀i i =1

Solving (5) leads to the optimal decision function as:

A. Basis of Support Vector Machine Support Vector Machine (SVM) was initially introduced by Vapnik and Chervonenkis at 1965. SVM is a supervised learning technique from the field of machine learning applicable to both classification and regression. In SVM, the training is reformulated and represented in such a way to obtain a quadratic programming problem whose solution is global and unique. Detailed information about SVM can be found in [18]. SVM is a binary classifier ݂ǣ ܴே հ ሼേͳሽ that is estimated by the given empirical data:

( x1 , y1 ) ,..., ( xm , ym ) ∈ R N × {±1}

separation between the classes. To construct this optimal hyperplane, we need to solve the following quadratic problem:

(3)

There are many hyperplanes separating data, but there exists a unique one yielding the maximum margin of

§ m · f ( x ) = sgn ¨ ¦ α i yi k ( xi , x ) + b ¸ © i =1 ¹

(6)

The decision function (6) shows that the classifier has an expansion in terms of a subset of the training data, namely those patterns whose ߙ௜ is non-zero, called support vectors. The non-linear mapping can be achieved introducing different so-called kernel functions, which computes the inner product of vectors Ԅሺ‫ݔ‬௜ ሻ and Ԅሺ‫ݔ‬௝ ሻ . The typical kernels functions include lineal, polynomial and radial basis functions. B. Multiclass SVM SVM inherently is a binary classifier and classifies the samples as being positive or negative. In many applications, such as limb motions classification is needed to solve multiclass problems. SVM performs very well for binary problems, and it is desirable to extend its capabilities into multiclass problems. Hence, we need a classifier ݂௞ ǣ ܴே հ ሼͳǡ ǥ ǡ ݇ሽ that estimates the most suitable class upon the given empirical data:

( x1 , y1 )1 ,..., ( xm , ym )k ∈ R N × {1,..., k }

(7)

Probably the simplest scheme for k-class classification problem is to train k independent binary classifiers that each one is trained to distinguish the training samples in one class against the all-remaining classes. To classify a new sample, k classifiers work separately, and one that outputs the largest decision function value is chosen as an estimated class. This scheme is referred to as the one-against-all. It is simple in implementation, fast in running and produces equal or more

2011 Third World Congress on Nature and Biologically Inspired Computing

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accurate results than other methods. Traaining with this scheme requires k binary SVMs. Another m method is the oneagainst-one. In this method, ݇ሺ݇ െ ͳሻȀʹ bbinary classifiers are trained to separate a pair of two classes. To classify a new sample, a class that gains more votees of the binary classifiers is chosen as the final output. III.

METHODOLOGY

A. Data Acquisition The myoelectric information was storeed in a database consisting of twelve healthy males subjectts (aged between 22 and 34). The protocol was approved by tthe Health Ethics Committee at our institution and subjects gave their informed consent. Nine motion classes were considered inn this study, these are related with 4 degrees of freedom of thhe forearm, wrist and hand (hand close/open, forearm pronnation/supination, wrist flexion/extension, and wrist radial/ulnaar deviation), and considering an inactive state. This last m motion class was acquired with the palm of the hand perppendicular to the floor, the fingers relaxed and cero degreees flexion of the wrist. The different motion classes weree recorded by a custom made four-channel signal condditioning system coupled with a 12 bit data acquisition card D DAQCard-6024E (National Instruments). Four-channel MES were recollected usinng square shaped disposable surface Ag-AgCl electroodes (Vermed, Mod.A10043) placed on different muscles as shown in Fig. 1. Each channel considers one pair of eleectrodes with an inter-electrode spacing of 1.5 cm. An extra electrode is also place on the elbow providing a referencee. Prior to signal acquisition, a conditioning stage was considdered, due to the detected MES on the skin has amplitudes rannging from 50ȝV to 5mV and frequency response from 20 too 500 Hz [27-28]. This stage has adjustable gain range from 100 to 5000 and CMRR greater than 96dB. All signals weere acquired in a laptop using LabVIEW software (National IInstruments) with a sampling rate of 1024Hz.

B. Protocol h to properly locate the A preliminary test session was held surface electrodes for MES measurements. m For this objective, the subject was asked to open and close the hand repeatedly to identify the adequate muscles areas of registration. After that, the skin waas properly cleaned with alcohol prior to place the four paiirs of electrodes and the reference. Once placed the electrod des; they were connected to the amplifier unit and it was con nfirmed that each channel would deliver a valid MES. At the beginning of the data acquisition session each subject was instructed to make the nine different motions he series of contractions. (see Fig. 2) in order to practice th Meanwhile, the amplification gain of the system was fixed by observing the raw signals to preevent the saturation at the channels output. The data acquisittion session consisted of recording fifteen times each motion class (135 records per on period the subject subject). During the registratio maintained a contraction of the forearm f muscles for 15 seconds. Due to the large numberr of motions, there was necessary to take a break of 1 min every e 12 records, in order to avoid fatigue. Each motion was w performed with the subject standing and the arm extended horizontally.

Figure 2. Motion classes: a) inactive, b) close, c c) open, d) pronation,e) supination, f) flexion, g) extension, h) raadial deviation and i) ulnar deviation.

IV.

Figure 1. Distribution of MES channels: (a) posteerior view and (b) anterior view. CH1- Extensor digiturum, CH2- Exteensor carpi radialis lungus, CH3- Flexor carpi radialis and CH4 - Flexxor carpi ulnaris. Reference electrode is place above the eelbow.

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DATA PROCESSING AND A MODELLING

A. Data Feature Extraction Most works related with MES M classification has considered the steady-stated ME ES recorded during a maintained contraction, where the record has a stochastic b quasi stationary [29]. character and can be expected to be Following this consideration all reecords employed in this work are of steady-state nature. Obtained information was possterior segmented using 50% overlapped windows of 250 0 ms, equivalent to 119 segments for each register measurred (formed only by the steady phase). From this informattion a feature extraction stage was performed, in order to obtain relevant features, t different movements. which allow the classification of the Two mainly analysis were selected to obtain features in both y domain. Features were the time domain and the frequency


extracted using Autoregressive (AR) features plus the Root Mean Square (RMS) and Discrete Fourier Transform (DFT) [1,30]. A total of 20 features for each analysis were obtained (5 from each of the four myoelectric channels) and organized in feature matrices of dimension (119,20) for each register. Considering the 15 measured records, each MES class has a feature matrix dimension of (1785,20). B. Classification models From the available information, different classifiers were built using LDA, ANN and SVM, in order to compare their classification performance. Each classifier was optimized for MES classification tasks and trained with 25%, 33.3% and 50% of the information and validated with the remaining data. On the one hand, final ANN model has an architecture consisting in 3 layers containing 9 neurons per layer and using the logsig transfer function in each one. The algorithm selected for the learning process was backpropagation and other parameters as learning rate and goal were set to a value of 0.01 and 1x10-6 respectively. On the other hand, SVM models were constructed considering one-against-all scheme and using a radial basis kernel function defined by (8):

(

k ( xi , x j ) = exp − xi − x j

2

2σ2

TABLE II. CLASSIFICATION RATE AND STANDARD DEVIATION OBTAINED WITH LDA, ANN AND SVM MODELS USING 50% OF TRAINING INFORMATION. LDA Motion Class

)

(8)

where xi and xj correspond to the testing and support vectors, the parameter σ determine the area of influence of the support vector has over the data. Its value was chosen from nine tested values ranging from 0.1 to 4, using as main criteria the classification performance and the total number of support vectors. This value was finally set to 1. All classification models and pre-processing stages were programmed by specific routines in Matlab® 2009b (Mathworks, Natick. MA, http://www.mathworks.com) programmed by the authors, and using appropriate either Neural Network or Signal Processing Toolboxes. V.

These reported results show that ANN and SVM models have better performance in classification tasks than LDA models regardless of the information amount used in training and testing stages. In this sense, using 50% of the total information for training and the remaining 50% for test, allows to obtain classifiers with better generalization ability. Table 2 summarizes the classification percentages and standard deviation of each model to identify independent movements using 50% of training data. It can be observed that LDA + time features accuracy is around 89.42% in the worst case (supination movement). The benefits of use frequency features are small compared to time features in LDA models. Nevertheless, for ANN and SVM models fed with frequency features show better performance than their counterparts fed with time features. In this case, the lowest classification values are 96.51% and 97.23%, while the other movements have a higher percentage even reaches values of 99.57% and 99.81% for each classifier respectively.

RESULTS

After training different classifiers using time and frequency features, the mean classification rate was calculated for the 9 motions described in the protocol section. Table 1 shows the results of the performance value obtained in the three defined schemes, using different amounts of training data.

(I ) (C) (O) (P) (S) (F) (E) (R) (U)

LDAt LDAf ANNt ANNf SVMt SVMf

Amount of Testing Data 75%

66.67%

50%

93.17 ± 4.20 94.31 ± 2.97 96.81 ± 2.42 97.82 ± 1.64 97.44 ± 1.84 98.47 ± 1.23

93.66 ± 4.06 94.44 ± 2.96 97.17 ± 2.20 98.13 ± 1.48 97.82 ± 1.67 98.66 ± 1.10

94.50 ± 3.71 94.62 ± 2.95 97.66 ± 1.85 98.51 ± 1.16 98.22 ± 1.35 98.95 ± 0.97

Frequency

99.14 ± 1.50 98.53 ± 2.65 91.84 ± 6.80 89.87 ± 6.23 89.42 ± 6.73 96.48 ± 3.04 97.70 ± 1.93 94.89 ± 3.50 92.60 ± 6.45

94.54 ± 3.20 98.95 ± 1.87 94.09 ± 5.26 90.98 ± 6.02 90.26 ± 6.18 96.84 ± 2.75 97.72 ± 2.46 95.36 ± 3.85 92.84 ± 6.40 ANN

Motion Class

(I ) (C) (O) (P) (S) (F) (E) (R) (U)

Inactive Close Open Pronation Supination Flexion Extension Radial deviation Ulnar deviation

Time

Frequency

98.66 ± 1.29 99.72 ± 0.24 95.64 ± 2.99 95.55 ± 4.42 95.05 ± 4.30 99.49 ± 0.53 99.32 ± 0.46 98.39 ± 1.74 97.10 ± 1.92

99.10 ± 0.59 99.57 ± 0.48 97.59 ± 1.88 97.14 ± 3.06 96.51 ± 3.05 99.52 ± 0.47 99.52 ± 0.33 99.15 ± 1.02 98.50 ± 0.99 SVM

TABLE I. CLASSIFICATION RATE AND STANDARD DEVIATION OBTAINED BY DIFERENT CLASSIFIERS. Classifier


Time

Motion Class

(I ) (C) (O) (P) (S) (F) (E) (R) (U)


Time

Frequency

98.58 ± 0.94 99.73 ± 0.21 96.69 ± 2.69 96.31 ± 3.74 96.80 ± 2.69 99.65 ± 0.39 99.55 ± 0.31 98.70 ± 1.31 97.95 ± 1.42

99.50 ± 0.46 99.81 ± 0.16 97.95 ± 1.91 98.04 ± 2.51 97.23 ± 2.59 99.78 ± 0.42 99.76 ± 0.22 99.51 ± 0.62 98.96 ± 0.65

a. The letters t and f indicates time and frequency respectively


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From these results, it is possible to establish that the use of frequency features (obtained by DFT analysis) gives useful information about MES components. Since DFT is an efficient version of Fourier Transform, its implementation in dedicated devices such as microcontrollers and FPGAs is also feasible. The high performance obtained in testing stages of the SVM classifier, allows us to think that this system can be used as multifunction myoelectric control. In this sense, the classification scheme based on frequency features plus SVM brings objective recognition of MES to identify independent motions. A graphical representation of the confusion matrix obtained using SVM and 50% of the available data is given in Fig. 3. First, it is worth noting that there are three areas where consistent misclassification occurs. These areas are related with open (O), pronation (P) and supination (S) motions. Here is possible to observe that the greater misclassification is present between S-P motions with around 1.24%, whereas the classes O-P and P-S present a 1.01% and 1.03% respectively. Other misclassification values can be observed between other motions; nevertheless their values are less than 0.6%. The discriminative power offered by SVM shows an appropriate performance in the distinction of different motions.

showed in the confusion matrix. Other motions such as close, flexion and extension don’t have this problem and require a smaller number of support vectors for their proper classification.

Figure 4. SVM model complexity reflected in the number of support vectors used in each motion class.

Figure 3. SVM Confusion Matrix. Gray scale at the right represents the classification rate obtained with the model. The main diagonal on figure is related to the correctly classified motion classes; meanwhile all the classification errors are represented by off-diagonal elements.

The complexity of the SVM models can be observed from the number of support vectors used to identify each class. Fig. 4 shows, different amounts of support vectors which are needed to identify the analyzed motions. Here it is possible to observe the minimum and maximum number of support vectors as well as their mean value, considering the 12 subjects who participated in this study. At the same time, it can be established that the complexity of the SVM model increases in the classification of open, pronation and supination motions. This information confirms the data

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VI. CONCLUSIONS Comparisons of six combinations of feature extraction / classifiers were analyzed in this work in order to establish the best myoelectric control strategy. The use of frequency features and SVM classifiers have shown better classification accuracy than other combinations using LDA or ANN classifiers fed with autoregressive features plus RMS or frequency features. These reported results were consistent even using a different amount of data for the testing stage. The application of this method employing four MES channels allowed the discrimination of 9 motion classes with an average misclassification rate of 1.53% and a standard deviation of 1.08. Even though the method proposed allowed the classification of 9 motion classes with high classification rate, the simultaneous control of multiple degrees of freedom of an anthropomorphic arm represents a more complex myoelectric control problem. This might be addressed by selecting a better feature extraction method or increasing the number of MES channels. Currently this is a research work in progress in our group. ACKNOWLEDGMENT Financial support for this work was provided by the Mexican National Council of Science and Technology CONACYT (Mexico) thorough doctoral scholarship and to the Instituto Politecnico Nacional for the support received during the M. León stay in the UCM (Madrid).


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