Comparison of Time-Domain and Frequency-Domain EMG Features for Gait Phases Classification Using Machine Learning

Accurate gait phase detection is essential for biomechanical analysis and the control of wearable assistive devices such as powered prostheses and exoskeletons. Electromyography (EMG) provides a direct representation of neuromuscular activation and offers potential advantages for low-latency, anticipatory gait phase recognition. However, the effectiveness of different EMG feature representations for stance-swing classification has not yet been clearly established. Therefore, this study presents a systematic comparison of time-domain (TD) and frequency-domain (FD) EMG features for gait phase classification. EMG signals were recorded from the tibialis anterior and medial gastrocnemius muscles of ten healthy participants during level walking. After preprocessing and segmentation, TD and FD features were extracted and used as inputs to a support vector machine classifier with a radial basis function kernel. Model performance was evaluated using a leave-one-subject-out cross-validation framework to assess generalization. The results demonstrate that TD features consistently outperform FD features across all evaluation metrics, achieving an accuracy of 0.813 ± 0.112, macro-averaged F1-score (Macro-F1) of 0.812 ± 0.114, and Matthews correlation coefficient (MCC) of 0.672 ± 0.178, compared to FD features with an accuracy of 0.712 ± 0.077, Macro-F1 of 0.708 ± 0.079, and MCC of 0.448 ± 0.159. These findings indicate that TD features more effectively capture the transient amplitude-based neuromuscular patterns associated with gait phase transitions. In addition, TD features offer lower computational complexity, making them well-suited for real-time implementation. Overall, this study highlights the superiority of time-domain EMG representations for reliable and efficient gait phase detection and provides practical guidance for the development of wearable gait monitoring and assistive control systems.