A Study on the Application of One Dimension Convolutional Neural Network for Classification of Gestures from Surface Electromyography Data

Myoelectric control systems are gaining popularity with the availability of commercial, low-cost, surface electromyography sensors. These systems can be used for gesture recognition which finds application in human-machine interfaces. The gestures are recognized using pattern recognition algorithms. Machine learning or deep learning techniques can be applied for the classification of gestures. In this paper, a user-specific 1-Dimensional Convolution Neural Network is proposed for the classification of Surface Electromyography data recorded using a commercially available surface electromyography recording device to perform offline classification of 5 hand gestures using limited data of less than 400 samples. An average accuracy of 82%±3% was achieved during the study after cross-validation of the data using 5-fold stratified cross-validation.