Classification of EMG Signals through Wavelet Neural Network for Finger-Robot Interface Maryam Alimohammadi Soltanmoradi, Vahid Azimirad*, Farajollah Tahernezhad-Javazm Department of Mechatronics, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran
[email protected],
[email protected]*,
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ABSTRACT The current paper presents Particle Swarm Optimized Wavelet Neural Network (PSOWNN) as a classification method for surface electromyogram (sEMG) pattern classification. According to the literature, a change in the spectrum of surface electromyogram has largely been attributed to the change in muscle conduction velocity. Therefore, such signals are used to command a robot using a WNN classifier. During the experiments, the subjects are instructed by an auditory cue to elicit a contraction from the rest state and hold that finger posture for a period of 5 seconds. For this purpose, two EMG electrodes attached to the human forearm are utilized to collect the EMG data. Time and frequency characteristics such as Number of Zero Crossings (ZC), Autoregressive (AR), and wavelet coefficients are considered as features. And, WNN as a classification method is optimized using particle swarm optimization algorithm. The accuracy of PSOWNN is compared to that of Artificial Neural Network (ANN). The results show an accuracy of 90% for the proposed method, indicating a better performance than ANN in terms of accuracy. Finally, outputs of the best classification method are implemented on a robot. Keywords: Electromyography signal, Wavelet Neural Network (WNN), Particle Swarm Optimization (PSO), Human-robot interface.
1.
INTRODUCTION
Electromyography signals contain rich information about human’s motion intention and general physiological state of a neuromuscular system, as they are increasingly considered in biomedical applications, prosthesis or rehabilitation devices, and human-machine interactions [1-4]. In previous studies, time, frequency, and time-frequency features were considered as feature extraction methods [5-7]. Classification methods include different techniques such as support vector machine (SVM), decision tree (DT), fuzzy logic, probabilistic neural network (PNN), k-nearest neighbor (KNN), multilayer perceptron (MLP), and so on [2, 8-10]. In [11], SVM is used for EMG signal classification and the accuracy rate of this method is about 80%. WNNs offer an acceptable compromise between robust implementations resulting from the redundancy characteristic of nonorthogonal wavelets and neural systems and efficient functional representations that build on the time-frequency localization property of wavelets [6]. Although PSOWNN applications in EEG signal classification were described by [12], the EMG related features were not considered and studied. The authors generalize the findings and employ this method for EMG signal classification. The disadvantage of previously-proposed methods is the need for a large amount of input data. On the other hand, fewer studies were reported to focus on finger movement than hand movement [2], [4], [7], [13-14]. And, there is no research work reported on the classification of EMG signals using PSOWNN. The present study focused on the classification of finger movement signals using a novel wavelet neural network with an optimization algorithm for human-machine interaction. First, the proposed method is presented. Then, the classification accuracy is calculated and compared for some features extracted from EMG signals (such as the number of zero crossings, autoregressive, and wavelet coefficients). The study innovation is using a smaller number of EMG channels and a new combination of PSO and WNN. Finally, the method is experimentally implemented on a robot. The rest of the present paper is organized as follows: Section 2 covers materials and methods, including data collection, feature extraction, classification, and optimization algorithms. Experimental results are presented in Section 3. And, Section 4 is the conclusion.
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2.
MATERIALS AND METHODS
2.1
Data Collection
The data are gathered from http://www.rami-khushaba.com. The EMG data are collected using two EMG channels and processed by the Bagnoli Desktop EMG Systems from Delsys Inc. The EMG signals collected from the electrodes are amplified using a Delsys Bagnoli-8 amplifier to a total gain of 1000. The signals are then bandpass filtered to 20-450 Hz with a notch filter implemented to remove the 50 Hz power line interference. During the experiment, the subjects are instructed by an auditory cue to elicit a contraction from the rest state and hold that finger posture for a period of 5 s. The two movements are considered as the database, i.e. flexion of Index (I-I) and the pinching of Thumb-Index (T-I). 2.2
Feature Extraction
The features set consisting of the time and frequency domain, Number of Zero Crossings, Autoregressive, and wavelet coefficients are proposed for analyzing the signal. The Daubechies (db) mother wavelet of order 45 is used for decomposing signals into four levels. Finally, ZC and AR of signal and wavelet coefficients (second (D2), third (D3), and fourth (D4-A4) level) are considered as features. In this study, the AR of order 5 is used and Yule-Walker equations are considered for calculating AR. The approximate and detailed coefficients of EMG signal taken from movement I-I of the first electrode are presented in Fig. 1.
Fig. 1. Approximate and detailed coefficients of EMG signal taken from movement I-I of the first electrode
2.3
Classification and Optimization Algorithm
WNN is a type of ANN based on wavelet function instead of conventional active function. The schematic diagram of WNN is shown in Fig. 2, where h represents Morlet function calculated as Equation (1): y e t
2
2
cos(5t ).
(1)
Where t is net b and net, a, b are inputs, weight, the dilation and the displacement factor, respectively. The a
weight of neurons between input and hidden layer, a, b are calculated using PSO algorithm. Here, the inverse of accuracy regarding the result of WNN is considered as the cost function. According to the global best position matrix, neural network weights and wavelet transform parameters are specified.
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Input 1 Input 2 . . . Input n Fig. 2. The schematic of Wavelet Neural Network (WNN)
3.
EXPERIMENTAL RESULTS The outputs of wavelet neural network are [1 0] and [0 1] representing I-I and T-I movements, respectively. The results of WNN with PSO are listed in Table 1, which are better than those of ANN. Table 1. Accuracy rate regarding features for WNN with PSO Features AR5 CZ D2 D3 D4 A4 AR5-D2 AR5-D3 AR5-D4 AR5-A4 CZ-D2 CZ-D3 CZ-D4 CZ-A4
Accuracies 79% 87% 66.7% 60% 75% 64% 75% 79.2% 75% 79% 75% 66.7% 66.7% 75%
The classifier is used in Tabriz-Puma robot using data acquisition card (Advantech PCI-1780U) as the interface of robot and computer, and C++ software is used for programming. The first link is set to be fixed and the second link of the robot is controlled by using EMG signals. The position of the link is read from the encoder. Table 2 includes the parameters of Tabriz-Puma robot.
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Table 2. Tabriz-Puma robot parameters Parameter Length (m) Weight (kg) Moment of Inertia (kg.m2)
First Link 0.7 4.585 0.007
Second Link 0.4 0.531 0.009
The best result of PSOWNN is used for controlling the robot. This setup is offline mode and is repeated multiple times. The motor speed and direction are set based on the type of finger movements. For I-I movement, the second link of the robot turns to left, and it turns to the right for the T-I movement. The experimental setup for this robot and results are shown in Fig. 3. According to the figure, the second link turns to left for output 1, and it starts to turn to the right for 0. The accuracy of the experimental test is about 90%.
Fig. 3 a) Tabriz-Puma robot controlling b) Results of classification in C++
4. CONCLUSION An EMG signal contains more important information, which is required in medical and physiological applications, such as diagnosis of neurological problems, biomedical and biochemical research, and prosthetic arm control. The present paper aimed to propose a new combination of PSO and WNN for electromyogram signal classification. It was proved that accuracy rate for the Zero-Crossings (ZC) feature was higher than other features ( 90%). The results indicated the benefit of using a smaller number of channels and validating WNN with Particle Swarm Optimization. The present study focused on the algorithms and methodologies used for detecting, processing and classifying EMG signals. The study also aimed to provide a clear and concise description of EMG processing methods for controlling prosthetic devices and improving the existing pattern recognition techniques. REFERENCES [1] R. H. Chowdhury, M. B. Reaz, M. A. B. M. Ali, A. A. Bakar, K. Chellappan, and T. G. Chang, “Surface electromyography signal processing and classification techniques,” Sensors, 13 (9), 12431-12466, 2013 [2] H.-B. Xie, T. Guo, S. Bai, S. Dokos, “Hybrid soft computing systems for electromyographic signals analysis: a review” Biomedical engineering online, 13(1), 8. , 2014 [3] J. Gutierrez, R. Munoz, “Wavelet neural network as EMG classifier”, In Health Care Exchanges (PAHCE), 2011 Pan American, 67-71, 2011.
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[4] S. Shin, R. Tafreshi, R. Langari, “A performance comparison of hand motion EMG classification”, 2014 Middle East Conference on in Biomedical Engineering (MECBME), 353-356, 2014 [5] R. N. Khushaba, S. Kodagoda, M. Takruri, G. Dissanayake, “Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications”, 39(12), 10731-10738, 2012 [6] A. Subasi, M.Yilmaz, H. R., Ozcalik, “Classification of EMG signals using wavelet neural network”, Journal of neuroscience methods, 156(1), 360-367, 2006 [7] S. Sharma, & Kumar, G., “Wavelet analysis based feature extraction for pattern classification from Single channel acquired EMG signal” Elixir Online Journal, 50, 320-321, 2012 [8] A. Subasi, “Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders” Computers in biology and medicine, 43(5), 576-586, 2013 [9] E. Gokgoz, and A. Subasi, “Comparison of decision tree algorithms for EMG signal classification using DWT,” Biomedical Signal Processing and Control, vol. 18, pp. 138-144, 2015. [10] S. Shin, R. Langari, and R. Tafreshi, "A Performance Comparison of EMG Classification Methods for Hand and Finger Motion", In ASME 2014 Dynamic Systems and Control Conference, pV002T16A008-V002T16A008, 2014. [11] M. A. Soltanmoradi, V. Azimirad, and M. Hajibabazadeh, "Detecting finger movement through classification of electromyography signals for use in control of robots", 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), 791-794, 2014. [12] V. Azimirad, M. Alimohammadi, A. Joudi, A. Eslami, and M. Farhoudi, “Analysis of PSO, AIS and GA-based optimal Wavelet-Neural Network classifier in Brain–Robot Interface,” IRBM, 36(4), 240-249, 2015. [13] D. Graupe, “Recognition and prediction of individual and combined muscular activation modes via surface EMG analysis” European Journal of Translational Myology, 20(3), 131-138, 2010 [14] M. Fatourechi, A. Bashashati, R. K. Ward, and G. E. Birch, “EMG and EOG artifacts in brain computer interface systems: A survey,” Clinical neurophysiology, 118(3), 480-494, 2007.
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