Recognition of Foot Gestures Through Time-Frequency Analysis and Deep Learning Techniques Using sEMG Data

Abstract

Most existing approaches for recognizing foot gestures rely on manually extracted features from sEMG recordings. This study aims to develop a deep learning-based method that automatically utilizes the time-frequency representation of sEMG signals from a single-channel sEMG, eliminating the need for manual feature extraction. To achieve this, the continuous wavelet transform (CWT) is used to generate time-frequency RGB images from sEMG signals. Transfer learning is then applied using convolutional neural networks, leveraging pre-recorded foot gesture signal data from healthy individuals. These CWT images are used to classify different foot gestures. The proposed method is evaluated on a dataset of foot gesture sEMG signals, and the results demonstrate that even with single- channel sEMG data, this approach can achieve near state-of-the-art accuracy.