sEMG-Based Motion Intention Recognition for Interactive Upper Limb Nursing Assistance

Surface electromyography (sEMG) enables non-invasive acquisition of neuromuscular activity and has shown strong potential for motion intention recognition in human–machine interaction. However, achieving reliable and real-time decoding remains critical for interactive upper-limb assistance. This study presents a structured sEMG-based framework for motion intention recognition in upper-limb assistance tasks, integrating multi-channel acquisition, standardized preprocessing, time-domain feature extraction, and supervised learning. sEMG signals from four representative motions were collected, and eight time-domain features were extracted from denoised and segmented signal windows. A compact feature subset was identified through systematic evaluation. Five classifiers were benchmarked under consistent validation conditions, with Random Forest achieving the best performance and further optimized via K-fold cross-validation. The proposed method achieved an average intra-subject accuracy of 95.23% across eight subjects and 95.72% in online interactive validation. These results demonstrate that time-domain feature fusion combined with ensemble learning provides robust and efficient motion discrimination, highlighting its potential for real-time assistive and rehabilitation applications.