Nonlinear Feature Decomposition and Deep Temporal–Spatial Learning for Single-Channel sEMG-Based Lower Limb Motion Recognition
Chunfeng Wei, Fayadh Alenezi, Jichi Chen, Hong Wang, Kemal Polat
Abstract
Lower limb motion recognition plays a vital role in intelligent prosthetic control and wearable assistance systems. While traditional methods typically utilize multi-channel surface electromyography (sEMG) signals to ensure recognition performance, such systems often involve complex hardware setups, are uncomfortable to wear, and require high computational resources. To address these challenges, this study aims to develop an efficient, low-complexity motion recognition approach based on single-channel sEMG signals. The proposed method integrates fast iterative filtering decomposition (FIFD) with a hybrid deep learning framework. FIFD is employed to decompose raw single-channel sEMG signals into multiple sub-components, allowing the extraction of informative intrinsic features. These features are then processed by a deep neural network that combines convolutional neural networks (CNN), attention mechanisms, and long short-term memory (LSTM) units to jointly capture spatial and temporal characteristics of the sEMG signal. Experiments were conducted on a self-constructed dataset comprising sEMG recordings of lower limb movements. The proposed method achieved a recognition accuracy of 99.35%, outperforming conventional multi-channel approaches and other baseline models. The model demonstrated strong robustness and generalizability using only single-channel input. This study presents a novel single-channel sEMG motion recognition method that significantly reduces system complexity while maintaining high recognition accuracy. The approach offers a promising solution for developing low-cost, efficient, and user-friendly wearable systems, particularly suitable for lower limb rehabilitation and human-computer interaction in resource-constrained environments.