a comparative study of wavelet families for classification of wrist motions

Computers and Electrical Engineering 38 (2012) 1798–1807

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Computers and Electrical Engineering

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A comparative study of wavelet families for classificationof wrist motions q

M. Hariharan a,⇑, C.Y. Fook a, R. Sindhu b, Bukhari Ilias a, Sazali Yaacob a

a School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Perlis, Malaysiab School of Microelectronic Engineering, University Malaysia Perlis (UniMAP), Perlis, Malaysia

a r t i c l e i n f o

Article history:Received 26 January 2012Received in revised form 25 August 2012Accepted 27 August 2012Available online 23 September 2012

0045-7906/$ - see front matter � 2012 Elsevier Ltdhttp://dx.doi.org/10.1016/j.compeleceng.2012.08.00

q Reviews processed and approved for publication⇑ Corresponding author.

E-mail address: [email protected] (M. Hariha

a b s t r a c t

The selection of most suitable mother wavelet function is still an open research problem invarious signal and image processing applications. This paper presents a comparative studyof different wavelet families (Daubechies, Symlets, Coiflets, and Biorthogonal) for analysisof wrist motions from electromyography (EMG) signals. EMG signals are decomposed intothree levels using discrete wavelet packet transform. From the decomposed EMG signals,root mean square (RMS) value, autoregressive (AR) model coefficients (4th order) andwaveform length (WL) are extracted. Two data projection methods such as principal com-ponent analysis (PCA) and linear disciminant analysis (LDA) are used to reduce the dimen-sionality of the extracted features. Probabilistic neural network (PNN) and generalregression neural network (GRNN) are employed to classify the different types of wristmotions, which gives a promising accuracy of above 99%. From the analysis, we inferredthat ‘Biorthogonal’ and ‘Coiflets’ wavelet families are more suitable for accurate classifica-tion of EMG signals of different wrist motions.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

With the advanced computer technology, Human Machine Interface (HMI) system has become an increasingly importantpart of our daily lives. In recent years, there has been a tremendous interest in introducing the user’s body movements andtranslate them into machine commands. Various biomedical signals such as Electroencephalogram (EEG), Electrooculogram(EOG) and EMG are used in developing rehabilitation or assistive device for the physically disabled [1,2]. Among these bio-medical signals, EMG signals can be used as a control source for an intuitive and natural HMI because EMG can be easilyacquired on skin with easy-to-apply surface electrodes. Development of physiological signal based rehabilitation or assistivedevice consists of two important stages. First stage includes acquiring physiological signal, signal processing and classifica-tion [3,4]. Second stage includes developing prototype of rehabilitation or assistive device. In this paper, initial stage ofdeveloping wrist EMG based rehabilitation or assistive device is proposed.

The organization of the paper is as follows: summary of the previous works is presented in Section 2. In Section 3, thebrief description of the proposed methodology and data collection procedure are discussed. Section 4 presents the featureextraction method used in this work. The fundamentals of data projection methods and classification algorithm are pre-sented in Sections 5 and 6. Experimental results and discussions are provided in Section 7. Finally, Section 8 concludesthe paper.

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by Editor-in-Chief Dr. Manu Malek.

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M. Hariharan et al. / Computers and Electrical Engineering 38 (2012) 1798–1807 1799

2. Previous works

There have been different feature extraction and classification algorithms proposed in the last decades for the analysis ofwrist EMG patterns. Table 1 presents some of the significant works on the analysis of wrist motions from EMG signals.

From the literature review, it has been observed that the feature extraction and classification algorithms play an impor-tant role in the area of wrist EMG analysis. It is difficult to compare previous works (Table 1), due to lack of uniformity incomputing and presenting the results. Different numbers of subjects are used for analysis. EMG is usually measured frombig muscular fibers such as arms and shoulders. However, some of the authors have measured EMG signals from wristand obtained lower classification of different types of wrist motions (52.5–90.5%). In this work, EMG signals of different wristmotions are measured from two muscles of the forearm (Extensor Digitorum and Flexor Carpi Ulnaris). The recorded EMGsignals of different wrist motions are decomposed into three levels using discrete wavelet packet transform. RMS values, ARmodel coefficients, and waveform length are extracted from decomposed wavelet packet coefficients [5]. Two data projec-tion methods (PCA and LDA) are used to remove the feature redundancy. Two types of radial basis neural networks (PNN andGRNN) [30] are employed for classifying different types of wrist motions. The performance of different mother wavelet func-tion on the classification performance is also presented.

3. Methods and data collection procedure

The classification of wrist EMG signals consists of four stages which includes recording of EMG signals of wrist motions,decomposition using discrete wavelet packet transform, feature extraction, feature reduction and classification of wrist mo-tions using PNN and GRNN classifiers. Fig. 1 shows the block diagram of the proposed system.

3.1. EMG electrode placement and signal acquisition

Several forearm muscles contribute to the movement of the wrist [5,14] and in this work two forearm muscles are iden-tified such as Extensor Digitorum (ED) and Flexor Carpi Ulnaris (FCU) for recording the movements of the wrist. FCU assistsin wrist flexion with ulnar deviation (up and down) and ED helps in extension of four fingers and aids in the extension ofwrist (open and grasp). EMG signals are recorded using ADInstrumentsML865 PowerLab 4/25T device. A total of five surfaceelectrodes (two channel pairs and a reference electrode) are adhered to each subject. Among them, two electrodes are ad-hered to the upper forearm of the subject and the other two are adhered to the lower forearm. Reliable wrist EMG signals arerecorded with a sampling frequency of 1000 Hz. The approximate position of electrodes placement on these muscles isshown in Fig. 2.

Table 1Summary of significant works on the analysis of wrist motions from EMG signals.

Authors Number ofsubjects

Feature extraction Classifier Bestaccuracy(%)

Range offrequency

Matsumura et al. [6] 3 Fast Fourier Transform (FFT) Backpropagation (BP) neuralnetwork and multiple principalcomponent analysis (MPCAN)

89.1 –

Matsumura et al. [7] 1 Fast Fourier Transform (FFT) Backpropagation (BP) neuralnetwork

72.86 –

Yazama et al. [8] 3 FFT, GA based feature band selection Neural network 52.5–89.1 40–1000 HzYazama et al. [9] 1 FFT, multidimensional directed information Neural network 75–85Matsumura et al. [10] 3 Multi discriminant analysis and gradual PCA Neural network 83.3–89.3 70–2000 HzKentaro et al. [11] 3 FFT, FFT, GA based feature band selection GA based neural network 86.3 70–2000 HzOyama et al. [12] 3 FFT, simple PCA and simple FLDA Neural network 90.5 70–2000 HzOyama et al. [13] 3 FFT with Incremental PCA Neural network 66.4–88.9 70–2000 Hz

EMG Signal Acquisition

EMG Signal Decomposition using

Discrete Wavelet Packet Transform

Feature Extraction &

Data Projection Methods

Classifiers (PNN and GRNN)

Classification of different types of wrist

motions

Fig. 1. Block diagram of the proposed system.

Fig. 2. Electrodes placement on subject.

Fig. 3. Types of wrist motions.

1800 M. Hariharan et al. / Computers and Electrical Engineering 38 (2012) 1798–1807

3.2. Data collection protocol

EMG signals of wrist motions are recorded from 10 healthy subjects (5 male and 5 female, age between 20 and 25 yearsold). Subjects are asked to repeat 5 wrist motions continuously for 15 trails. During the data acquisition the subjects are in-structed not to make any obvious movements and keep their body relaxed. The subjects are requested to perform five tasks,namely up, down, neutral, open and grasp as discussed below:

Task 1 – up: Subjects are asked to perform wrist extension from neutral position for 5 s.Task 2 – down: Subjects are asked to perform wrist flexion from neutral position for 5 s.Task 3 – neutral: Subjects are asked to maintain neutral position for 5 s.Task 4 – open: Subjects are asked to perform fingers extension with maximum torque from neutral position for 5 s.Task 5 – grasp: Subjects are asked to perform fingers flexion with maximum torque from neutral position for 5 s. The rawEMG signals have amplitudes of the order of 0–5 mV and contain useful frequency components of up to 500 Hz. Fig. 3shows the types of wrist motions.

4. Feature extraction using discrete wavelet packet transform

In the recent years, wavelet transform has been used to analyze all kinds of problems in image and signal processing.Physiological signal (EMG) processing is one of these areas. EMG signal is a highly non-stationary signal. In frequency do-main analysis of non-stationary signals, the time domain information is lost while performing the frequency transformation.It is not easy to tell when a particular event took place from the Fourier transformed signals. Wavelet transform approach is agood tool for analyzing non-stationary signals, as it provides a simple approach for dealing with local aspects of a signal bothin time and frequency scale [15,34]. Hence, wavelet analysis has the potential for the classification of wrist motions. Thissection briefly explains the design of wavelet packet filters and the feature extraction using them.

4.1. Wavelet packets

Discrete wavelet transform (DWT) provides time frequency representation of the signal which decomposes the signalover dilated and translated wavelets. A wavelet is a small waveform that has an average value of zero with limited duration.The wavelet transform uses multi-resolution technique by which different frequencies are analyzed with distinct resolution[16–18]. In DWT decomposition procedure, a signal is decomposed into two frequency bands such as lower frequency band(approximation coefficients) and higher frequency band (detail coefficients). DWT gives a left recursive binary tree structureas in DWT decomposition procedure, low frequency band is used for further decomposition [18]. Wavelet packet (WP) givesa balanced binary tree structure as in WP decomposition procedure, both lower and higher frequency bands are decomposedinto two sub-bands [15]. In the tree, each subspace is indexed by its depth i and the number of subspaces p. The two waveletpacket orthogonal bases at a parent node (i, p) are given by the following forms [19,20].

w2piþ1ðkÞ ¼

X1n¼�1

l½n�wpi ðk� 2inÞ ð1Þ

Table 2Frequency band of each level.

Wavelet packet decomposition level Frequency band (Hz)

1 0–250, 250–5002 0–125, 125–250, 250–375, 375–5003 0–62.5, 62.5–125, 125–187.5, 187.5–250, 250–312.5, 312.5–375, 375–437.5, 437.5–500


where l[n] is a low pass (scaling) filter.

w2pþ1iþ1 ðkÞ ¼

X1n¼�1

h½n�wpi ðk� 2inÞ ð2Þ

where h[n] is the high pass (wavelet) filter. Wavelet packet decomposition helps to partition the high frequency side intosmaller bands which cannot be achieved by using general discrete wavelet transform. Table 2 summarizes the frequencybands of each level which has been decomposed. In our analysis, the sampling frequency is 1000 Hz.

After three level wavelet packet decomposition, RMS value, 4th order autoregressive coefficient and waveform length areextracted from the wavelet packet coefficients [5,15]. Based on the experimental investigations, the best level of decompo-sition is found to be 3.

4.2. Root mean square

Root mean square is modeled as amplitude modulated Gaussian random process, whose RMS is related to the constantforce and non-fatiguing contraction [5,19]. RMS values of the wavelet packet coefficients can be expressed as

RMSm ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1N

XN

i¼1

Cim;k

� �2

vuut m ¼ 1;2; . . . ;M k ¼ 0;1; . . . ;2M � 1 ð3Þ

where m represents the number of decomposition level, C is the wavelet packet coefficients, N is the number of waveletpacket coefficients at each level and k represents wavelet packet node.

4.3. Autoregressive coefficient

Autoregressive model describes each sample of surface EMG signal as a linear combination of previous samples plus awhite noise error term. The model is basically of the following form:

yðtÞ ¼Xm

i¼1

aiyðt � iÞ þ eðtÞ ð4Þ

where ai are the model coefficients, m is the order of the model and e is the output error. A fourth order AR model coefficientsis used [20] as our features, which is adequate for modeling EMG signals, thus generating four features for each channel ofwrist EMG signal.

4.4. Waveform length

Waveform length is the cumulative length of the waveform over the time segment. WL is related to the waveform ampli-tude, frequency and time [5]. Waveform length of wavelet packet coefficients can be expressed by

WLm ¼XM

m¼1

XN�1

j¼1

jCjþ1m;k � Cj

m;kj m ¼ 1;2; . . . ;M k ¼ 0;1; . . . ;2M � 1 ð5Þ

where m represents the number of decomposition level, C is the wavelet packet coefficients and k represents wavelet packetnode, N represents the number of wavelet packet coefficients at each level. After extracting the above features from waveletpacket coefficients, a feature vector is created as shown in:

Feature vector ¼ ½RMS AR1 AR2 AR3 AR4 WL� ð6Þ

The recorded EMG signals are decomposed into three levels which gives eight sub-bands. From each sub-band, six fea-tures are extracted and hence a total number of 48 features are obtained from each channel.

5. Data projection methods

Analysis of features is a key step of any pattern recognition problems, since it is closely related to the classifier perfor-mance and complexity [21]. Generally, there are two feature analysis approaches such as sequential methods and projection


methods, which are used for reducing the original dimension of the feature space. Sequential methods do not remove thefeature redundancy completely since the final solution depends on the extracted feature set. In projection methods, projec-tion is performed by a linear or non-linear transformation [20]. In this paper, two projection methods (PCA and LDA) are usedfor reducing the original dimension of the feature space [4,20].

5.1. Principal component analysis

PCA is a well-known linear projection method and is widely used in many applications for dimensionality reduction[22,35]. PCA performs the vector projection without any knowledge of their class labels. Hence it is an unsupervised dataanalysis method. PCA reveals about the hidden information from the original feature space by maximizing the variance ofthe projected vectors. First, the covariance matrix is computed from the original feature set. Next, the eigenvectors andeigenvalues of covariance matrix are computed and sorted according to the decreasing eigenvalue. Principal components(PCs) are computed and number of PCs are selected based on the eigenvalue criterion [23,35]. Only the PCs of eigenvaluegreater than ‘1’ are retained and used to transform the original dataset into the space of the selected principal components,which gives lower dimensional feature dataset. After applying PCA, the number of features is reduced to 7, since first seveneigenvalues are greater than ‘1’ and has the greater amount of variance.

5.2. Linear discriminant analysis

The main objective of LDA is to minimize the distances among the vectors belonging to the same class and to maximizethe distances among the class centers. LDA utilizes supervised projection method to find a set of base vectors (zi), in such away that the ratio of the between and within class scatters of the training sample set is maximized. This is an equivalent tosolving the following generalized eigenvalue problem [4,24,25].

Zopt ¼ arg maxz

jZT SbZjjZT SwZj

¼ ½z1; z2; . . . ; zP� ð7Þ

where {zi|1 6 i 6 P} are the LDA subspace base vectors, P is the dimension of the subspaces Sb and Sw are represented by thefollowing equations:

With-in class scatter Sw ¼Xc

i¼1

Xxk2Xi

ðxk � liÞðxk � liÞT ð8Þ

Between class scatter Sb ¼Xc

i¼1

Niðli � lÞðli � lÞT ð9Þ

where c is the number of classes. x 2 RN is a data sample. Xi is the set of samples with class label i. li is the mean for the all thesamples with the class label i. Ni is the number of samples in the class i. When Sw is non-singular, the base vectors Z sought inthe above equation are the first P most ‘‘significant’’ eigenvectors of S�1

w Sb, that correspond to the P largest eigenvalues {ki1 -6 i 6 P}. For a given test sample x, we can obtain its representation in LDA subspace by a simple linear projection WTx be-cause the LDA base vectors are orthogonal each other [4,24,25]. After employing LDA feature reduction, the number offeatures is reduced from 48 to 4.

6. Classifiers

Artificial Neural Networks (ANNs) are widely used for classification, approximation and control problems. Different neu-ral network models are available for classifying the patterns. ANNs provide alternative form of computing that attempts tomimic the functionality of the brain [26,36]. In this work, two types of radial basis neural networks (PNN and GRNN) areemployed for classifying different types of wrist motions, since these networks have been successfully applied in differentpattern recognition applications [18,27–32]. Radial basis function networks use exponential activation function instead ofsigmoidal function that compute activations as a distance measure (usually the Euclidean distance or a weighted norm) be-tween the input vector and a prototype vector, which characterizes the signal function at a hidden neuron rather thanemploying an inner product between the input vector and the weight vector [28–33]. The target variable is categoricalfor PNN classifier and continuous for GRNN classifier. The brief description of the fundamentals of PNN and GRNN classifiersare as follows:

6.1. Probabilistic neural network

Specht has proposed the probabilistic neural net based on Bayesian classification and classical estimators for probabilitydensity function [27–32]. PNN consists of four layer, such as input layer, pattern layer, summation layer and output layer.The input layer does not perform any computation and simply distributes the inputs to the neurons of pattern layer. All


the neurons in the PNN architecture are fully interconnected [18,30]. Neurons of the pattern layer are activated by exponen-tial function and the number of neurons in the pattern units is equal to the number of training samples. When an input ispresented, the neurons of the pattern layer computes distances from the input vector to the training input vectors and pro-duces a vector whose elements indicate how close the input is to a training input. Neurons of the summation layer sum thesecontributions for each class of inputs and produce a net output which is a vector of probabilities [27]. The number of neuronsin the summation units is equal to the number of classes. From the maximum of the outputs of summation units, outputunits produce a ‘1’ for that class and a ‘0’ for the other classes using compete transfer function.

The performance of the PNN classifier highly depends upon the smoothing parameter or spread factor (r). Varyingsmoothing parameter (r) gives control over the degree of non-linearity of the decision boundaries for the network. A deci-sion boundary approaches a hyperplane for large values of r and approximates the highly non-linear decision surface of thenearest neighbor classifier for small values of r that are close to zero. Based on the experimental investigations, the r value isvaried between 0.03 and 0.12 in steps of 0.01.

6.2. General regression neural network

GRNN is a kind of radial basis networks and the training is conducted using one pass learning. This network does not re-quire an iterative training procedure; it presents much faster learning than multilayer perceptron (MLP), it is more accuratethan MLP and relatively insensitive to outliers [34]. Specht has proposed the model of GRNN to perform general (linear ornon-linear) regressions [32]. GRNN is based on the theory of probability regression analysis. It usually uses Parzen windowestimates to set up the probability density function (PDF) from the observed data samples. x is a random vector variable, y isa random scalar variable, X and Y are measured values, f(x, y) is the known continuous joint PDF. The expected value of y (theregression value on X) is given by [30,32].

EðyjXÞ ¼R1�1 yf ðX; yÞdyR1�1 f ðX; yÞdy

ð10Þ

where y = the output predicted by GRNN, X = the input vector (x1, x2, . . . , xn) which consists of n predictor variables,E(y|X) = the expected value of the output y given an input vector X, and f(X, y) = the joint probability density function of Xand y.

The estimated value Y is an exponentially weighted average value of all observed values Yi given as in [32]:

YKðxÞ ¼

Pni¼1Yi exp � D2

i2r2

� �

Pni¼1 exp � D2

i2r2

� � ð11Þ

where Di is defined as in

D2i ¼ ðX � XiÞT � ðX � XiÞ ð12Þ

The variable r is a smoothing parameter that can be made large to smooth out noisy data or small to allow the estimatedregression surface to be as non-linear as it is required to approximate closely the actual observed values of Yi. The GRNN hasfour different layers: input layer, pattern layer, summation layer and output layer. The performance of the GRNN classifierhighly depends upon the smoothing parameter or spread factor (r). Based on the experimental investigations, the r value isvaried between 0.03 and 0.12 in steps of 0.01. All the feature extraction and classifications are developed under MATLABenvironment [37].

7. Results and discussion

The recorded EMG signals are decomposed into three levels and it gives eight subbands. Six features are extracted fromeach subband, thereby a total of 48 features are obtained from each channel. The database of wrist motions is made up of 10volunteers. Each subject is asked to repeat five motions continuously for 15 times. Two channels are used to record the EMGsignals of wrist motions. The total size of dataset is 1500 � 48 (2 channel � 10 volunteers � 15 trails � 5 motions � 48). Inthis work, 10-fold cross validation schemes is used to prove the reliability of the classification results, where the proposedfeature vectors are divided randomly into 10 sets and training is repeated for 10 times. Two types of radial basis neural net-works (PNN and GRNN) are used for classifying different types of wrist motions. The networks are trained with original fea-ture set as well as reduced feature set which is obtained after applying two data projection methods (PCA and LDA). GRNNand PNN are trained with different spread factor or smoothing parameter from 0.03 to 0.12. Classification results of GRNNand PNN for different wavelet families are presented in Tables 3–6. Average and standard deviation of the classification accu-racies of different types of wrist motions are tabulated. The standard deviation of the classification clearly reveals the con-sistency of the classifier results. If the standard deviation is higher, the classification results will be inconsistent and thisinconsistency depends on optimal learning parameters of the classifiers as well.

Table 3Classification results of GRNN and PNN for different orders of ‘Daubechies’ wavelets.

Different orderof Daubechies

Type ofclassifier

Featurereduction

Neutral Down Up Grasp Open Average

db2 GRNN Original 96.33 ± 1.55 94.70 ± 1.86 95.73 ± 1.67 95.87 ± 1.48 95.17 ± 1.93 95.56 ± 1.67PCA 96.10 ± 1.30 95.13 ± 2.01 95.73 ± 1.44 95.47 ± 1.28 96.10 ± 1.73 95.71 ± 1.53LDA 96.97 ± 0.90 94.90 ± 0.21 96.97 ± 0.41 95.23 ± 1.71 88.53 ± 4.08 94.52 ± 0.77

PNN Original 95.03 ± 0.18 93.33 ± 0.00 94.70 ± 0.10 94.87 ± 0.31 93.70 ± 0.18 94.33 ± 0.06PCA 96.00 ± 0.37 88.73 ± 5.66 96.50 ± 0.76 76.17 ± 13.31 90.83 ± 1.83 89.65 ± 3.91LDA 96.27 ± 0.42 94.67 ± 0.15 96.77 ± 0.21 94.40 ± 1.49 85.90 ± 6.70 93.60 ± 1.48









Table 4Classification results of GRNNand PNN for different orders of ‘Symlet’ wavelets.

Different orderof Symlets

Type ofclassifier

Featurereduction


sym2 GRNN Original 96.37 ± 1.59 94.63 ± 1.89 95.77 ± 1.59 96.00 ± 1.63 95.10 ± 2.07 95.57 ± 1.74PCA 96.30 ± 1.06 95.23 ± 2.06 95.80 ± 1.63 95.67 ± 1.42 96.23 ± 1.82 95.85 ± 1.56LDA 97.10 ± 0.90 94.97 ± 0.23 96.93 ± 0.36 95.20 ± 1.59 88.97 ± 3.74 94.63 ± 0.70









Table 5Classification results of GRNN and PNN for different orders of ‘Biorthogonal’ wavelets.

Different order ofBiorthogonal

Type ofclassifier

Featurereduction


bior1.1 GRNN Original 98.27 ± 1.48 97.17 ± 2.14 97.93 ± 1.91 97.97 ± 1.64 97.60 ± 2.25 97.79 ± 1.86PCA 97.60 ± 0.71 96.67 ± 1.76 96.60 ± 1.94 96.37 ± 1.35 96.43 ± 1.86 96.73 ± 1.48LDA 99.37 ± 0.74 98.93 ± 0.83 99.43 ± 0.75 98.37 ± 1.22 99.23 ± 0.45 99.07 ± 0.76








Table 6Classification results of GRNN and PNN for different orders of ‘Coiflet’ wavelets.

Different order ofCoiflet

Type ofclassifier

Featurereduction


coif1 GRNN Original 98.90 ± 1.59 97.97 ± 1.86 98.47 ± 1.55 98.70 ± 1.57 98.23 ± 2.07 98.45 ± 1.67PCA 96.47 ± 1.52 95.60 ± 1.65 96.43 ± 1.41 95.80 ± 1.38 96.10 ± 1.44 96.08 ± 1.42LDA 99.47 ± 0.67 98.73 ± 1.27 99.70 ± 0.31 99.40 ± 0.70 99.27 ± 0.57 99.31 ± 0.67












From the Table 3, the best average classification accuracy is found to be above 97% for ‘db8’, ‘db10’ and ‘db20’ using GRNNclassifier with original 48 features. After applying PCA, the best average classification accuracy of above 96% for ‘db4’, db8’,‘db10’, ‘db20’ using GRNN classifier only with seven features is attained. The best average classification of above of 94% for‘db2’ and ‘db4’ using GRNN classifier, after applying LDA is achieved. On the whole, GRNN gives better accuracy than PNNclassifier. After applying the two data projection methods, we observed that there is a drop in accuracy (2% in LDA and1% in PCA).

From the Table 4, the best average classification accuracy of above 96% for ‘sym6’ and ‘sym8’ using GRNN classifier withoriginal 48 features is attained. The maximum average classification accuracy of above 96% is obtained for ‘sym4’ and ‘sym8’after applying PCA using GRNN classifier. The percentage of drop in accuracy is very less after applying PCA. After employingLDA, the maximum average classification accuracy of 94% is achieved for ‘sym2’ and ‘sym4’ using GRNN classifier.

From the Table 5, it can be inferred that the best average classification accuracy of 99% is obtained for ‘bior3.3’ and‘bior4.4’ using GRNN classifier with original 48 features. The best average classification accuracy of above 96% is attainedfor all the different orders of Biorthogonal family after applying PCA. After employing LDA, the best average classificationaccuracy of 99% is achieved for ‘bior1.1’ and ‘bior2.2’ using GRNN classifier. Four features are obtained by applying LDA pro-jection methods, which gives a classification accuracy of 99% since LDA is minimizing the distances among the vectorsbelonging to the same class and maximizing the distances among the class centers. From the Table 6, it can be deduced thatGRNN classifier gives the best average classification accuracy of 99% for all the orders of Coiflets family using the original 48features except ‘coif1’. After applying PCA, the best average classification accuracy of above 96% is achieved for all the ordersof Coiflets family. The best average classification accuracy of above 99% is obtained for ‘coif1’ and ‘coif4’ after employing LDAusing GRNN classifier.

From the Tables 3–6, it is observed that the GRNN outperforms the PNN classifier in terms of classification accuracy. Fromthe experimental results, it is also found the ‘Biorthogonal’ and ‘Coiflet’ wavelet families are apt for the classification of dif-ferent types of wrist motions from EMG signals. The results of the current work cannot be directly compared with earlierwork, since the placement of the electrodes for recording the EMG signals of wrist motions is distinct from the earlier worksand also due to lack of uniformity in computing and presenting the results. In order to prove the reliability of the classifi-cation results, 10-fold cross validation is performed.

8. Conclusions

Accurate classification of wrist motions from EMG signals is still an open problem among the researchers. The proposedstudy is to investigate and propose appropriate mother wavelet family for accurate classification of wrist motions from EMGsignals. EMG signals are subjected to feature extraction using wavelet packet transform with different mother wavelet func-tions. We have also used two dimensionality reduction methods to transform the original high-dimensional feature spaceinto low-dimensional space for accurate classification of wrist motions without loss of classification accuracy. After applyingPCA and LDA, the dimension of original feature set (48 features) is reduced to 7 and 4 respectively. In order to test the effec-tiveness and reliability of the original and reduced feature set, two types of radial basis neural networks (GRNN and PNN) areapplied for the classification of five types of wrist motions. The best average classification accuracy of above 99% is achievedfor classification of five types of wrist motions from EMG signals using GRNN classifier. From the experimental results, it canbe concluded that the ‘Biorthogonal’ and ‘Coiflets’ wavelet families are more suitable for the analysis of EMG signals of dif-ferent wrist motions.

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M. Hariharan is working as a Senior Lecturer in School of Mechatronic Engineering, University Malaysia Perlis, Malaysia. He received Ph.D. degree inMechatronic Engineering, UniMAP, Malaysia in 2010. He has published more than 50 papers in peer-reviewed journals and conferences. His researchinterests include speech signal processing, biomedical signal and image processing, and artificial intelligence. He is a member of IEEE, USA.

Chong Yen Fook is a postgraduate student in Universiti Malaysia Perlis, Malaysia. He has obtained Bachelor of Engineering in Biomedical ElectronicEngineering from the UniMAPin 2011. His research interests include biomedical signal processing, artificial intelligence, speech recognition, BCI applica-tions. He is a student member of IEEE, USA.

R. Sindhu is a Postgraduate student in Universiti Malaysia Perlis, Malaysia. She has completed her Bachelor of Technology in Information Technology fromAnna University in 2009. Her research interest includes machine learning algorithms and hybrid optimization algorithms.

Bukhari Ilias is a Lecturer in School of Mechatronic Engineering, University Malaysia Perlis, Malaysia. He has obtained Bachelor of Engineering (Hons) inMechatronic Engineering from Middlesex University, London and Master degree from Universiti Malaysia Perlis, Malaysia. His research interests includerobotics, automotion, embedded system, SLAM and signal processing.

Sazali Yaacob is currently working as a Professor at Universiti Malaysia Perlis, Malaysia. He has published more than 150 peer-reviewed journals andconferences. His research interests are in Artificial Intelligence applications in the fields of acoustics, vision and robotics. He has received Charted Engineerstatus by the Engineering Council, United Kingdom in 2005 and also a member of the IET (UK).

http://www.mathworks.com

a comparative study of wavelet families for classification of wrist motions

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