J. Cent. South Univ. (2013) 20: 960−968 DOI: 10.1007/s11771­013­1571­2

Comparison of wrist motion classification methods using surface electromyogram

JEONG Eui­chul, KIM Seo­jun, SONG Young­rok, LEE Sang­min

Department of Electronic Engineering, Inha University, yonghyun­dong, Incheon 402­751, Korea © Central South University Press and Springer­Verlag Berlin Heidelberg 2013

Abstract: The Gaussian mixture model (GMM), k­nearest neighbor (k­NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non­normalization in GMM, k­NN, and LDA.

Key words: Gaussian mixture model; k­nearest neighbor; quadratic discriminant analysis; linear discriminant analysis; electromyogram (EMG); pattern classification; feature extraction

Foundation item: Project(NIPA­2012­H0401­12­1007) supported by the MKE(The Ministry of Knowledge Economy), Korea, supervised by the NIPA; Project(2010­0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea

Received date: 2012−09−03; Accepted date: 2013−01−10 Corresponding author: LEE Sang­min, Professor, PhD; Tel: +82−32−860−7420; E­mail: sanglee@inha.ac.kr

1 Introduction

Device­control studies using electromyogram (EMG) have been increasing in HCI technology and rehabilitation engineering for amputees [1]. For this reason, we have to understand the characteristics of EMG signals, and acquisition, processing, analysis technology is necessary. There are invasive and non­invasive methods to acquire EMG signals. The invasive method involves surgery and is difficult. On the other hand, a non­invasive method is excellent, cost­effective and more convenient to use [2]. Thus, surface EMG is used to motion classification in this work.

EMG is an electrical signal that occurs during muscle contraction. A nerve impulse generated in the spinal cord activates motor neuron, thereby a motor unit action potential is generated by activating muscular fiber in the motor unit. This phenomenon can be extracted by the spatial sum [3].

EMG signals generated from the wrist muscle have been widely used in clinical neurology, neuromuscular, motor control, motor disturbance, gait analysis, and exercise physiology [4]. In previous studies, FARRY et al [5] classified two discrete motions namely chuck and key grasp using multi­window taper methods.

ENGLEHART et al [6] classified six discrete wrist and finger motions using time­frequency representations, such as wavelet, in the EMG. Also, PELEG et al [7] studied finger activity classification based on a k­nearest neighbor using two electrode pairs placed on the forearm. CHAN et al [8] researched on the six upper limb motions based on a Gaussian mixture model. BAKER et al [9] made a study of the macaque monkey’s finger motions based on linear discriminant analysis. CHAN et al [10] investigated a fuzzy logic system to classify single­sit EMG signals for multifunctional prosthesis control. ALKAN et al [11] studied a discriminant analysis and support vector machine to classify four arm motions.

In this work, the Gaussian mixture model (GMM), k­nearest neighbor (k­NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) which classify wrist motions such as up, down, left, right, and rest using surface EMG were compared. The selection of the important features in pattern classification was conducted in the time domain, which is simple to calculate process, not in the frequency domain. Extracted features were DAMV (difference absolute value) [12], DASDV (difference absolute standard deviation value) [13], MAV (mean absolute value) [14], RMS (root mean square) [15]. Sixteen

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feature sets were composed of a combination of two features which were obtained from Channel 1 and Channel 2 placed on the forearm. In order to select the feature which showed the best performance of classification, feature sets were compared for each classification method. Extracted feature sets were normalized, then normalized feature sets and non­normalized feature sets were compared to verify that normalization was better than non­normalization.

The experiment was conducted with twenty eight healthy subjects (23 males and 5 females) in order to verify the wrist motion classification algorithm. After the motion classification was performed using learning signals which were obtained from EMG signals of four wrist motions and a rest state, the performance of the motion classification was confirmed by input non­learning signals.

2 Materials and methods

2.1 Subjects In this work, EMG signals were recorded from

twenty­eight healthy subjects (23 male and 5 female) with ages ranging from 20 to 38 years (24.5±4.95). They were informed about experimental procedures before starting the study.

2.2 Experiment protocol In this work, we classify wrist motion such as up,

down, left, right, and rest. Wrist motions are shown in Fig. 1. In order to acquire EMG signals, EMG electrodes (Ag/AgCl) are placed on the flexor carpi ulnaris muscle for Channel 1 and extensor carpi ulnaris muscle for Channel 2, as shown in Fig. 2. The amplitude of EMG signals can range in 0−10 mV (peak­to­peak) or 0−15 mV (RMS). The usable energy of the signal is limited

to 0−500 Hz frequency range, with dominant energy being in 50−150 Hz [16]. In this work, MP 150WSW and BN­EMG2 (BIOPAC Systems, Inc., USA) were used to acquire EMG signals. The obtained EMG signals were put through a band­pass filter of 10−500 Hz bandwidth, and were sampled at 1 kHz to classify wrist motion.

Fig. 2 Electrode position for electromyogram acquisition

When the experiment begins, subjects who have EMG electrodes attached at their forearm take a rest for 5 s, and then perform each wrist motion during 5 s. They rest for 5 s in between each wrist motion and EMG signals are collected 45 s of total acquisition time, by adding 5 s of rest at the end. The time­window size for feature extraction is important element for EMG signals processing. An EMG time­window is typically 100−200 ms. In this work, the size of the time­window was selected to be 166 ms. Thirty features per each motion of obtained EMG signals were extracted by using the data in the time­window and normalized, and then used to classify wrist motion. After 24 h, the subjects conducted the same experiment in order to verify the performance of the classification algorithm. The GMM, k­NN, QDA, and LDA algorithm are used to classify wrist motions.

In this work, comparison between each classification methods was conducted. The difference between performances of classification methods was confirmed by comparing the results of fixed features on each classification method. Also, classification accuracies of features were compared to verify effects of feature selection on EMG signal processing. The combined feature set of the best accuracy of classification was found by applying the feature sets to fixed classification method. And, classification accuracy of normalization and non­normalization was compared. Before extracted features were applied to the classification method, it is verified that the result of normalization is better than the result of non­normalization by normalizing. The flow chart of the motion classification algorithm is shown in Fig. 3.

Fig. 1 Wrist motion: (a)

Up; (b) Down; (c) Left;

(d) Right; (e) Rest

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Fig. 3 Flow chart of motion classification algorithm

2.3 Feature extraction An EMG signal is a complicated signal influenced

by various factors, such as physiological and anatomical properties and characteristics of instrumentation [17]. Thus, the success of any pattern classification system depends almost entirely on the choice of features used to represent the raw signals [12]. Many previous studies have been analyzed by Fourier transformation in the frequency domain. However, if the analyzed signal is unstable, it is not suitable for Fourier transformation. Thus, there is a necessary assumption that the signal should be stable. This means that Fourier transformation is not suitable for non­stable EMG signals. Fourier transformation has the added disadvantage that the calculation process is complex. Therefore, in this work, because it has been widely used in recent research and has been simplified in the calculation process, features are extracted in the time domain.

Features are extracted by using the following methods:

1) Difference absolute mean value (DAMV) This is the average of the difference between two

adjacent samples of EMG signals: 1

1 1

1 | |

1

N

k k k

D X X N

−

+ =

= − − ∑ (1)

2) Difference absolute standard deviation value (DASDV)

This is the standard deviation between two adjacent samples of EMG signal:

( ) 1

1 1

1 1

N

k k k

V X X N

−

+ =

= − − ∑ (2)

3) Mean absolute value (MAV) This is the average of the absolute value of the

EMG signal:

1

1 | |

N

k k

M X N =

= ∑ (3)

4) Root mean square (RMS) This is the average of the square of the EMG signal:

2

1

1 N k

k R X

N = = ∑ (4)

where N is the number of data sample in the time­window and Xk is k­th data sample in the time­window.

2.4 Classification methods 2.4.1 Gaussian mixture model

The GMM is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMM is commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal­tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative expectation­maximization (EM) algorithm or maximum A­posteriori (MAP) estimation from a well­trained prior model.

GMM is a weighted sum of component Gaussian densities as given by

( ) ( , ) i i i p g λ ω = ∑ x x µ Σ (5)

where x is a D­dimensional continuous­valued data vector (i.e. measurement or features), ωi, i=1, ∙∙∙, M, are the mixture weights, and g(x| µi, ∑i), i=1, ∙∙∙, M, are the component Gaussian densities. Each component density is a D­variate Gaussian function of the form,

1 2 2

1 1 ( , ) exp ( ) ( )

2 (2π)

i i i i i D

i

g ′ = − − − x x x µ Σ µ Σ µ

Σ (6)

with mean vector µi and covariance matrix ∑i. The

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mixture weights satisfy the constraint that 1

1 M

i i

ω =

= ∑ .

The complete GMM is parameterized by the mean vectors, covariance matrices and mixture weights from all component densities. These parameters are collectively represented by the notation,

, , ), =1, , i i i i M λ ω ⋅⋅⋅ = µ Σ (7)

There are several variants in the GMM shown in Eq. (3). The covariance matrices, ∑i, can be full rank or constrained to be diagonal. Additionally, parameters can be shared, or tied, among the Gaussian components, such as having a common covariance matrix for all components. The choice of model configuration (number of components, full or diagonal covariance matrices, and parameter tying) is often determined by the amount of data available for estimating the GMM parameters and how the GMM is used in a particular biometric application.

It is also important to note that because the components of the Gaussian act together to model the overall feature density, full covariance matrices are not necessary, even if the features are not statistically independent. The linear combination of diagonal covariance basis Gaussians is capable of modeling the correlations between feature vector elements. The effect of using a set of M full covariance matrix Gaussians can be equally obtained by using a larger set of diagonal covariance Gaussians. 2.4.2 k­nearest neighbor

The k­NN classification algorithm is a method of classifying objects based on closet training examples. This is an eidetic method which classifies unlabeled samples depending on similarity among training datasets. That is to say, unlabeled sample xu∈R D if given, the k­NN computes the distance between xu and all the data points in the training data, and assigns the class including k training samples which are the nearest neighbor label data of xu. Suppose that a training dataset of n points with their desired class is given:

x1, y1,x2, y2,∙∙∙, xn, yn (8)

where xi, yi represents data pair i, with xi as the feature vector and yi as the corresponding target class. Then for a new data point x, the most likely class should be determined by k­NN, as follows:

kNN(x)=yp, p=argmini|xi −xi| 2 (9)

The preceding equation uses the nearest neighbor to determine the class. In this work, the Euclidian distance method was selected because it is often used as the distance metric. The k­value was fixed at 1. 2.4.3 Linear and quadratic discriminant analysis

The LDA is a method to reduce the dimension of a

feature vector in a manner that maximizes the ratio of the between­class variance to the within­class variance. At this point, if the variance­covariance matrix of a normal distribution is identical regardless of the category, this case is called LDA, since the linear discriminant function is derived. But, if the variance­covariance matrix differs by category, this case is called QDA since the quadratic discriminant function is derived.

Discriminant analysis finds out the transformation matrix W that minimizes the within­class variance, in order to find a linear transformation matrix W that maximizes the objective function J(W). In other words, an approximation W is obtained as an optimization problem:

2 T 1 2 B 2 2 T 1 2 B

| | ( )

( ) u u J s s

− = =

+

% %

% % W S W W W S W

(10)

where 1 2 | | u u − % % is the distance between the centers of the projected data, and 2 2

1 2 ( ) s s + % % is the within­class variance of the projected data.

The differential objective function J(W) is zero in order to find the maximum of the objective function, and this equation is calculated by the solution of a generalized eigenvalue problem:

1 W 1 2 ( ) u u − = − W S (11)

An optimized transformation matrix Wopt is acquired by maximization theorem:

T B

opt T W

atg max = = W S W

W W S W

(12)

where SB is the between­class variance matrix and SW is the within­class variance matrix.

T B W

1 1 ( )( ) ,

c c

i i i i i i = =

= − − = ∑ ∑ S n m m m m S S (13)

where m represents the average of the sample of total, and c is the number of classes.

2.5 Data analysis Features are selected one per each channel from

EMG signals which are obtained from two channels. In the previous studies, EMG signals were classified using single feature. That is to say, the same feature was used to classify EMG signals in Channel 1 and Channel 2. However, in this work, 16 double features, which are the combination of features of Channel 1 and feature of Channel 2, were formed by not only 4 homogeneous double features using same feature but also 12 heterogeneous double features using different features of each channel. They were used to classify EMG signals in order to inquire into the various cases of features.

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Table 1 16 double features (4 homogeneous double features, and 12 heterogeneous double features) combined in two features Channel 2

Channel 1 DAMV DASDV MAV RMS

DAMV (DAMV, DAMV) (DAMV, DASDV) (DAMV, MAV) (DAMV, RMS) DASDV (DASDV, DAMV) (DASDV, DASDV) (DASDV, MAV) (DASDV, RMS) MAV (MAV, DAMV) (MAV, DASDV) (MAV, MAV) (MAV, RMS) RMS (RMS, DAMV) (RMS, DASDV) (RMS, MAV) (RMS, RMS)

Combined double features are shown in Table 1. Because the EMG signals depend on the force, scale of EMG signals is needed to normalize. In order to normalize the scale, the following normalization was conducted:

nean

mean mean

min max min V

V − ′ =

− (14)

mean mean max min − normalization method must take the extracted data from feature set and transform amplitude of EMG signals that depend on the forces into significant value. V is the extracted feature from the EMG signals, and V' is the normalized feature. maxmean and minmean are maximum and minimum of the feature average that correspond to each wrist motion. Normalized features are rearranged in the specified range.

The accuracies of wrist motions are compared statistically using a T­test. The T­test was conducted as the follows.

1) Between the feature of the highest accuracy and the lowest accuracy in each motion classification;

2) Among motion classification methods; 3) Between normalization and non­normalization. There are two methods, such as two­sample

assuming equal variance and two­sample assuming unequal variance in the T­test. Before performing the T­test, the difference of variance was identified between two samples by F­test. As a result of the F­test, if there was no difference of variance, a two­sample assuming equal variance T­test was conducted. On the other hand, if there was a difference of variance, a two­sample assuming unequal variance T­test is conducted.

3 Results and discussion

3.1 Motion classification accuracy In this work, the accuracy of classification was

derived by wrist motion classification methods such as GMM, k­NN, QDA, and LDA. The classification methods are widely used in previous research. The classification methods are trained using features extracted from the EMG signals of the subject. The performance of the classification methods was verified by inputting non­learning data, which are the same as the experiment results, after 24 h.

The accuracy graph of the motion classification methods is shown in Fig. 4. The accuracy of each

classification method is calculated as the average of all cases, which can be configured as a combination of two of four features. When performing normalization, the accuracies were (95.36±3.9)% for GMM, (95.25±4.42)% for k­NN, (93.55±5.53)% for QDA, and (91.86±5.66)% for LDA. If normalization is not performed, the accuracies were (93.95±5.19)% for GMM, (93.95±6.64)% for k­NN, (93.06±5.8)% for QDA, and (91.39±7.12)% for LDA. Overall, GMM and k­NN showed better than the QDA and LDA. The classification methods in this work are identical to the previous studies, but the experiment method differs from previous studies and classification accuracy is reliable in comparison with previous studies.

Fig. 4 Results of decision to perform normalization and motion classification methods

The results of the motion classification depending on features are shown in Fig. 5. The feature sets of the highest accuracy and the lowest accuracy were different for each of the classification methods. In the case of GMM, k­NN, and QDA, (MAV, DAMV) showed the highest accuracy and (DASDV, RMS) indicated the lowest accuracy. In the case of LDA, the feature set of the highest accuracy was (RMS, DASDV) and the feature set of the lowest accuracy was (DASDV, MAV).

3.2 Statistical analyses The comparison of the accuracy of results from the

GMM, k­NN, QDA, and LDA classification methods showed that the GMM and k­NN had higher accuracies, while QDA and LDA had relatively lower accuracies. And, the normalized results showed better than the

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Fig. 5 Results of motion classification by feature sets

non­normalized results. Also, depending on the feature selection, there was a difference in classification accuracy.

The F­test and T­test were performed to compare the classification results. The F­test was implemented to discriminate the difference of variance among classes, and the type of the T­test was decided depending on the result of the F­test.

First, the comparison of the accuracy by feature selection was conducted in accordance with F­test and T­test. In the case of GMM, k­NN, and QDA, the double feature that showed the highest accuracy was (MAV, DAMV), and the lowest accuracy was shown in (DASDV, RMS). As a result of the F­test between (MAV, DAMV) and (DASDV, RMS) in GMM and k­NN, there was a significant difference of variance (p<0.05). Therefore, a two­sample assuming unequal variance T­test was conducted, and the result of the T­test was that the accuracy of classification between (MAV, DAMV) and (DASDV, RMS) showed a significant difference (p< 0.05). In the case of QDA, there was no difference of variance between (MAV, DAMV) and (DASDV, RMS) by F­test (p<0.05). As a result of the two­sample assuming equal variance T­test, there was no difference between (MAV, DAMV) and (DASDV, RMS). In the case of LDA, the double feature that showed the highest accuracy was (RMS, DASDV), and the lowest accuracy was shown in (DASDV, MAV). As a result of the F­test, there was no significant difference between (RMS, DASDV) and (DASDV, MAV) (p<0.05). Thus, the two­sample assuming equal variance T­test was conducted, and the significant difference was nonexistent (p<0.05). This has shown that feature selection is very important in the motion classification process. Depending on the feature selection, the performance of motion classification was shown to be different. Table 2

shows the results of the F­test and Table 3 shows the result of the T­test between double features. The method showed that the best classification was (MAV, DAMV) of GMM, the worst classification was shown in (DASDV, MAV) of LDA. As a result of the F­test, there was difference of variance between them (p<0.05), and the significant difference was verified by the two­sample assuming unequal variance T­test (p<0.05).

Also, the difference of classification accuracy between homogeneous double feature and heterogeneous double feature was confirmed. The homogeneous double feature that showed the highest accuracy was DAMV in GMM and k­NN and was DASDV in QDA. The accuracy of RMS was the highest in RMS. In the case of the heterogeneous double feature, (MAV, DAMV) was the highest in GMM, k­NN, and QDA. But, (RMS, DASDV) was the highest in LDA. Table 4 and Table 5 show the F­test and T­test, respectively. Between homogeneous double feature and heterogeneous double feature, there was no significant difference (p<0.05).

Second, the comparison of the classification methods was carried out using the F­test and T­test. As a result of the F­test, a significant difference of variance was verified between GMM and LDA, and between QDA and LDA (p<0.05). Meanwhile, there was no difference of variance between GMM and k­NN, GMM and QDA, k­NN and QDA, k­NN and LDA (p<0.05). As a result of the two­sample assuming unequal variance T­test between GMM and LDA, QDA and LDA, and the two­sample assuming equal variance T­test between GMM and QDA, k­NN and QDA, k­NN and LDA, there was a significant difference (p<0.05). However, there was no difference between GMM and k­NN (p<0.05). Table 6 shows the results of the F­test and Table 7 shows the result of the T­test among the classification methods.

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Table 2 Result of F­test between double feature of highest accuracy and lowest accuracy GMM k­NN QDA LDA

Item (MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(RMS, DASDV)

(DASDV, MAV)

Average 96.85 93.58 96.61 93.74 94.8 91.92 92.91 90.73 Variance 14.65 28.83 14.05 35.5 35.74 49.5 36.13 41.03

Number of subjects 28 28 28 28

F ratio 0.51 0.4 0.72 0.88 p­value (F­test) 0.04 0.009 00.2 0.37 F critical value 0.52 0.52 0.52 0.52

Table 3 Result of T­test between double feature of highest accuracy and lowest accuracy GMM k­NN QDA LDA

Item (MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(RMS, DASDV)

(DASDV, MAV)

Average 96.85 93.58 96.61 93.74 94.8 91.92 92.91 90.73 Variance 14.65 28.83 14.05 35.5 35.74 49.5 36.13 41.03 Number of subjects 28 28 28 28

T ratio X X 42.62 38.58 p­value (T­test) 2.62 2.16 1.65 1.31 T critical value 0.01 0.04 0.1 0.2

X: Statistically nonsignificant results between two groups (p>0.05).

Table 4 Result of F­test between homogeneous double feature and heterogeneous double feature GMM k­NN QDA LDA

Item (MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(RMS, DASDV)

(DASDV, MAV)

Average 96.77 96.85 96.21 96.61 94.8 94.8 92.16 92.91 Variance 10.12 14.65 17.03 14.05 31.62 35.74 36.38 36.13 Number of subjects 28 28 28 28

F ratio 0.69 1.21 0.88 1 p­value (F­test) 0.17 0.31 0.38 0.49 F critical value 0.52 1.9 0.52 1.9

Table 5 Result of T­test between homogeneous double feature and heterogeneous double feature GMM k­NN QDA LDA

Item (MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(MAV, DAMV)

(DASDV, RMS)

(RMS, DASDV)

(DASDV, MAV)

Average 96.77 96.85 96.21 96.61 94.8 94.8 92.16 92.91 Variance 10.12 14.65 17.03 14.05 31.62 35.74 36.38 36.13 Number of subjects 28 28 28 28

Pooled variance 12.38 15.54 33.68 36.26 T ratio 0.08 0.37 0.000 0.46

p­value (T­test) 0.94 0.71 0.99 0.65 T critical value 2 2 2 2

Third, difference of accuracy between normalization and non­normalization was examined. As a result of F­test, variance was not different between normalization and non­normalization in GMM, k­NN, QDA, and LDA (p<0.05). Therefore, two­sample assuming equal variance T­test was performed, and result of T­test was

that accuracy between normalization and non­ normalization shows a significant difference in GMM, k­NN and LDA (p<0.05). However, there was no difference in QDA (p<0.05). Table 8 represents results of F­test and Table 9 shows result of T­test between normalization and non­normalization.

J. Cent. South Univ. (2013) 20: 960−968 967

Table 6 Result of F­test among classification methods GMM k­NN GMM QDA GMM LDA k­NN QDA k­NN LDA QDA LDA Item

Average 95.36 95.25 95.36 93.55 95.36 91.86 95.25 93.55 95.25 91.86 93.55 91.86

Variance 1.47 0.9 1.47 1.07 1.47 0.62 0.9 1.07 0.9 0.62 1.07 0.62 Number of feature sets 16 16 16 16 16 16

F ratio 1.79 1.29 3.79 0.72 2.11 2.93

p­value (F­test) 0.13 0.31 0.007 0.27 0.08 0.02

F critical value 2.4 2.4 2.4 0.4 2.4 2.4

Table 7 Result of T­test among classification methods GMM k­NN GMM QDA GMM LDA k­NN QDA k­NN LDA QDA LDA Item

Average 95.36 95.25 95.36 93.55 95.36 91.86 95.25 93.55 95.25 91.86 93.55 91.86

Variance 1.47 0.9 1.47 1.07 1.47 0.62 0.9 1.07 0.9 0.62 1.07 0.62 Number of feature sets 16 16 16 16 16 16

T ratio 1.15 1.31 X 0.98 0.61 X

p­value (T­test) 0.29 4.49 10.25 4.87 12.32 5.45

T critical value 0.77 0.000 0..000 0.000 0.000 0.000

X: Statistically nonsignificant results between two groups (p>0.05).

Table 8 Result of F­test between normalization and non­normalization GMM k­NN QDA LDA

Item Normali­ zation

Non­ Normali­ zation

Normali­ zation

Non­ Normali­ zation

Normali­ zation

Non­ Normali­ zation

Normali­ zation

Non­ Normali­ zation

Average 95.36 93.95 95.25 93.95 93.55 93.06 91.86 91.39 Variance 1.47 0.71 0.85 0.88 1.14 1.36 0.39 0.39 Number of feature sets 16 16 16 16

F ratio 2.07 0.93 0.94 0.99 p­value (F­test) 0.09 0.45 0.37 0.49

F critical value 2.4 0.4 0.4 0.4

Table 9 Result of T­test between normalization and non­normalization GMM k­NN QDA LDA

Item Normali­ zation

Non­ Normali­ zation

Normali­ zation

Non­ Normali­ zation

Normali­ zation

Non­ Normali­ zation

Normali­ zation

Non­ Normali­ zation

Average 95.36 93.95 95.25 93.95 93.55 93.06 91.86 91.39

Variance 1.47 0.71 0.85 0.88 1.14 1.36 0.39 0.39 Number of feature sets 16 16 16 16

Pooled variance 1.09 0.85 1.25 0.39

T statistic 3.8 3.97 1.24 2.14 p­value (T­test)

0.000 0.000 0.22 0.04

T critical value 2.04 2.04 2.04 2.04

4 Conclusions

1) GMM, k­NN, QDA, and LDA methods were

compared to classify wrist motion using surface EMG. Because of the simplicity of the calculation process, DAMV, DASDV, MAV, and RMS were used as features. Classification accuracies were (95.36±3.9)% for GMM,

J. Cent. South Univ. (2013) 20: 960−968 968

(95.25±4.42)% for k­NN, (93.55±5.53)% for QDA, and (91.85±5.66)% for LDA. Overall, the GMM and k­NN were better than QDA and LDA. Through the T­test, it was shown that the performance of GMM and k­NN were statistically better than the QDA and LDA (p<0.05). A significant difference was determined between normalization and non­normalization. Also, the classification performances in each classification method were significantly different by feature selection (p<0.05). Consequently, the (MAV, DAMV) of GMM showed the highest accuracy, and the lowest accuracy was represented in the (DASDV, RMS) of LDA.

2) An advanced method that can classify more diverse motions than up, down, left, right, and rest will be developed in the future. Also, a motion classification method for the lower limbs as well as upper will be researched. This will allow the motion classification method to be developed in the future.

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(Edited by YANG Bing)