Hand Gesture Classification through Time Frequency Images with One-to-One, All-to-One, and Cross-Validation Approaches.

Abstract

Surface electromyographic (sEMG) signals are a non-invasive method for acquiring signals that play a fundamental role in the monitoring of prosthetic devices by providing information about human motor functions. This leads to the need for accurate classification of sEMG signals, despite variations in signal stationarity, the presence of sensor noise, differences between the muscles involved, and the peculiarities of each patient. This study focuses on the classification of hand grip postures using sEMG signals acquired from amputee patients. Special emphasis is placed on the use of the time-frequency domain for feature extraction, using the spectral analysis of the reduced-time Fourier transform (STFT).To carry out this task, a classification model based on a convolutional neural network (CNN) is used. The classification method is adjusted, trained, and evaluated through three experiments. The first, called "One to One", yields accuracy percentages of 90.84%, 91.05%, and 91.13% for spectrograms of 32x32, 64x64, and 128x128 in size, respectively. In the second validation, called "All by One", an accuracy of 62.28% is achieved for spectrograms of 32x32 pixels. Finally, in the last K-fold cross-validation validation, an average accuracy of 86.73%, 86.77%, and 87.97% is obtained for spectrograms of 32x32, 64x64, and 128x128 in size, respectively.