Machine Learning Approaches for EMG Signal Classification to Predict Hand Gesture

In this study, the hand gestures prediction for electromyography (EMG) signals classification is provided. EMG signals which are electrical stimulations of muscles were measured, processed and analyzed to achieve discriminative features in time, frequency and time-frequency domain. To improve a better result of the classification, a number of classifiers namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF) and Artificial Neural Network (ANN) have been compared with each other with the objective of hybrid feature combination to enhance the accuracy of the result of the classification. As it turned out, ANN proved to be the most precise in classification (96.2 percent), followed by SVM (94.6 percent), RF (92.7 percent) and k-NN (89.4 percent). These findings suggest that hybrid feature extraction works better in terms of discriminative power and nonlinear learning capacity of ANN can be employed to extract the complicated and non-stationary nature of EMG signals. Overall, this research paper indicates the potential of hybrid feature extraction in conjunction with sophisticated learning architectures to enhance performance, robustness and flexibility of EMG-based hand gesture recognition systems to be applied in the field of prosthetic control, rehabilitation robotics as well as human-machine interfaces.

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