Gesture recognition using electromyography and deep learning

Human gesture recognition using electromyography (EMG) signals holds high potential in enhancing the functionality of human-machine interfaces, prosthetic devices, and sports performance analysis. This work proposes a gesture classification system based on electromyography. This system has been designed to improve the accuracy of forearm gesture classification by leveraging advanced signal processing and deep learning techniques to optimize classification accuracy. The system is composed of two main modules: a signal processing module able to perform several transforms (Short-Time Fourier Transform and Constant-Q-Transform) and a classification module based on Convolutional Neural Networks (CNNs). The dataset employed in this study "Latent Factors Limiting the Performance of sEMG-Interfaces" comprises EMG signals collected via a bracelet equipped with 8 distinct sensors, capable of capturing a wide range of forearm muscle activities. The experimental process is composed of two main phases. Firstly, we employed a k-fold cross-validation methodology to systematically assess and validate the model's performance across different subsets of the data for hyperparameter tunning. Secondly, the best system configuration was evaluated over a new subset reporting significant improvements. The baseline neural network architecture reported an accuracy of 85.0 ± 0.13 % in classifying gestures. Through rigorous hyperparameter tuning and the application of various mathematical transformations to the EMG features, we managed to enhance the classification accuracy to 90.0 ± 0.12 % (an absolute improvement of 5% compared to the baseline for a 5-class problem). When comparing to previous works, we improved the F-score from 85.5%, to 89.3% for a 4-class problem (left, right, up and down).