MG Signals Using Combined Features and Soft Computing Techniques
Mr. Omkar Deshpande1,Dr. D. S. Dhote2, Dr. G. D. Agrahari2
1Research Scholar, Department of Electronics, Brijlal Biyani Science College, Amravati.
2Department of Electronics, Brijlal Biyani Science College, Amravati.
E-mail: omkardeshpande957@gmail.com
Abstract:
Electromyography (EMG) is a widely used technique for analyzing muscle activity and has significant applications in prosthetics, rehabilitation, and neuromuscular disorder diagnosis. The classification of EMG signals remains a challenging task due to their complex, non-stationary nature and susceptibility to noise. To improve classification accuracy, this study employs a hybrid approach that integrates multiple feature extraction techniques with soft computing methods. The proposed methodology involves data acquisition from multiple subjects performing different hand and forearm movements. The raw EMG signals are preprocessed using noise filtering, segmentation, and normalization techniques to ensure high-quality input data. A comprehensive feature extraction process is then applied, combining time-domain features such as Mean Absolute Value, Root Mean Square, Waveform Length, and Zero Crossing, along with frequency-domain features including Mean Frequency, Median Frequency, Power Spectral Density, and Wavelet Coefficients. This combined feature set provides a more detailed representation of the EMG signals, capturing both temporal and spectral characteristics essential for effective classification. The study highlights the importance of integrating diverse feature extraction techniques to enhance EMG signal interpretation. The findings contribute to the development of more accurate EMG-based control systems for assistive technologies, including prosthetic devices and rehabilitation tools. By leveraging a comprehensive approach to EMG signal processing, this research aims to improve muscle activity classification and enable more precise control of biomedical applications. Soft computing techniques, including ANN, SVM Systems, have shown great potential in handling the inherent variability of EMG signals. These methods leverage learning-based approaches to model complex patterns and improve classification accuracy. By integrating time-domain and frequency-domain features, a more comprehensive representation of the EMG signal can be achieved, enabling soft computing models to perform better in distinguishing different muscle activities. Future research will focus on optimizing deep learning techniques for real-time EMG classification and extending the approach to broader applications such as brain-computer interfaces and human-computer interaction. The proposed methodology offers a promising step toward the development of intelligent, high-performance EMG-based systems.
Keywords: Electromyography, EMG Signal Classification, Feature Extraction, Soft Computing, Artificial Neural Networks, Support Vector Machines.
DOI link – https://doi.org/10.69758/GIMRJ/2505I5VXIIIP0040
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