An efficient approach for physical actions classification using surface EMG signals
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Health Information Science and Systems
RESEARCH
An efficient approach for physical actions classification using surface EMG signals Sravani Chada, Sachin Taran and Varun Bajaj*
Abstract Physical actions classification of surface electromyography (sEMG) signal is required in applications like prosthesis, and robotic control etc. In this paper, tunable-Q factor wavelet transform (TQWT) based algorithm is proposed for the classification of physical actions such as clapping, hugging, bowing, handshaking, standing, running, jumping, waving, seating, and walking. sEMG signal is decomposed into sub-bands by TQWT. Various features are extracted from each different band and statistical analysis is performed. These features are fed into multi-class least squares support vector machine classifier using two non-linear kernel functions, morlet wavelet function, and radial basis function. The proposed method is an attempt for classifying physical actions using TQWT and its performance and results are promising and have high classification accuracy of 97.74% for sub-band eight with morlet kernel function. Keywords: Surface electromyography (sEMG), Tunable-Q factor wavelet transform (TQWT), MC-LSSVM, Physical actions Introduction The accidental injuries and neurological diseases are responsible for losing the limb functions in human being [1]. With the advancement in recent technology, many bionic rehabilitation hands can be developed to help patients in restoring some or all of the lost motor functionality [1]. The physical actions require assistance between skeletal and muscular systems [2]. For every physical action, skeletal muscles generate currents that can be measured in terms of electromyography (EMG) signal. EMG signals can be recorded in two ways either by inserting needles directly into the muscles called needle electromyography (nEMG) or by placing skin surface electrodes above the muscle called surface electromyography (sEMG) [3]. The sEMG shows better performance for controlling the limb movement and it can be used as a signal source for prosthesis control [1]. sEMG signals are useful in biomedical engineering and man-machine interfaces for various applications like prosthesis and
*Correspondence: [email protected] Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 452005, India © Springer Nature Switzerland AG 2019.
robotic devices control, diagnosis of neuromuscular diseases [4], wheelchairs, virtual mouse and world etc. Features extracted from the mode of variational mode decomposition and used as input to multi-class least squares support vector machine (MC-LSSVM) classifier with radial basis function (RBF) kernel which classifies physical actions [5]. Sixteen features like integrated EMG, root mean square value, mean absolute value have been reduced using principal component analysis and classified by adaptive neuro-fuzzy inference system [6]. sEMG signals are windowed and features like fourth order autoregressive
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