Year: 2026
Type: article
Abstract: 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.
Source: Sensors
DOI: 10.3390/s26103021
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7160844532