Direct comparison of SVM and LR classifier for SEMG signal classification using TFD features

Abstract The performance of any bio-medical signal based robotic device depends upon the classification accuracy of the system. In this context, surface electromyography (SEMG) signals acquisition was done from 15 right hand dominated healthy subjects. After the data acquisition, some pre-processing steps like smoothing, rectification and normalization was performed followed by Daubechies 4 (db4) discrete wavelet transform (DWT) de-noising. DWT was also utilized for SEMG signals decomposition up to four levels. DWT decomposed the SEMG signal in approximation and detailed coefficients and finally fourth level approximation and detailed coefficients were exploited for time-frequency domain features extraction. The performance of individual time-frequency domain features was critically compared with support vector machine (SVM) classifier and linear regression (LR) model. The results indicated that the performance of SVM classifier was found better than the LR classifier. SVM classifier achieved the 95.8% classification accuracy with all combined time-frequency domain features in the form of the feature vector.

[1]  Yogendra Narayan.  SEMG signal classification using KNN classifier with FD and TFD features , 2020 .

[2]  Oluwarotimi Williams Samuel,et al.  Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors , 2017, Journal of Medical Systems.

[3]  Lini Mathew,et al.  Development of a software module for feature extraction and classification of EMG signals , 2015, 2015 Communication, Control and Intelligent Systems (CCIS).

[4]  Abdulhamit Subasi,et al.  Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition , 2020, Biomed. Signal Process. Control..

[5]  Honghai Liu,et al.  Surface EMG data aggregation processing for intelligent prosthetic action recognition , 2018, Neural Computing and Applications.

[6]  Babita,et al.  Binary movement classification of sEMG signal using linear SVM and Wavelet Packet Transform , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[7]  Yogendra Narayan,et al.  Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification , 2020 .

[8]  Jenny C. Castiblanco,et al.  EMG-driven hand model based on the classification of individual finger movements , 2020, Biomed. Signal Process. Control..

[9]  S. Chatterji,et al.  sEMG signal classification with novel feature extraction using different machine learning approaches , 2018, J. Intell. Fuzzy Syst..

[10]  D. Power,et al.  Dilution of Seawater Affects the Ca2 + Transport in the Outer Mantle Epithelium of Crassostrea gigas , 2020, Frontiers in Physiology.

[11]  Anish C. Turlapaty,et al.  Feature Analysis for Classification of Physical Actions Using Surface EMG Data , 2019, IEEE Sensors Journal.

[12]  Shallu,et al.  Elbow movement classification of a robotic arm using wavelet packet and cubic SVM , 2016 .

[13]  Mahmoud Tavakoli,et al.  Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach , 2017, Expert Syst. Appl..

[14]  Winnie Jensen,et al.  The effect of time on EMG classification of hand motions in able-bodied and transradial amputees. , 2018, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[15]  A. Phinyomark,et al.  Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification , 2011 .

[16]  Denis Kouame,et al.  New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices , 2014, Comput. Math. Methods Medicine.

[17]  Li Zhang,et al.  Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks , 2014, Expert Syst. Appl..

[18]  Gang Wang,et al.  The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method , 2014, Comput. Math. Methods Medicine.

[19]  Lini Mathew,et al.  Discrete Wavelet Packet based Elbow Movement classification using Fine Gaussian SVM , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[20]  Kazuo Kiguchi,et al.  Evaluation of Fuzzy-Neuro Modifiers for Compensation of the Effects of Muscle Fatigue on EMG-Based Control to be Used in Upper-Limb Power-Assist Exoskeletons , 2013 .

[21]  Suman Samui,et al.  An experimental study on upper limb position invariant EMG signal classification based on deep neural network , 2020, Biomed. Signal Process. Control..

[22]  Ravinder Agarwal,et al.  SEMG signal analysis at acupressure points for elbow movement. , 2011, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[23]  S. Chatterji,et al.  Surface EMG Signal Classification Using Ensemble Algorithm, PCA and DWT for Robot Control , 2018 .

[24]  Yogendra Narayan.  Hb vsEMG signal classification with time domain and Frequency domain features using LDA and ANN classifier , 2020 .

[25]  Preeti Kumari,et al.  Advance approach towards elbow movement classification using discrete wavelet transform and quadratic support vector machine , 2016 .

[26]  Abdul Rahim Abdullah,et al.  Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals , 2018, Machines.

[27]  Panagiotis K. Artemiadis,et al.  An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features , 2010, IEEE Transactions on Information Technology in Biomedicine.

[28]  Yang Zhang,et al.  Pattern recognition of SEMG based on wavelet packet transform and improved SVM , 2019, Optik.

[29]  Neelesh Kumar,et al.  Fusion of EEG and EMG signals for classification of unilateral foot movements , 2020, Biomed. Signal Process. Control..

[30]  E. Scheme,et al.  Fractal Analysis of Human Gait Variability via Stride Interval Time Series , 2020, Frontiers in Physiology.