Enhancing the Prediction of Locomotion Transition with High-Density Surface Electromyography

• Published in: IEEE journal of biomedical and health informatics pp. 1 - 13
• Main Authors: Jing, Shibo, Huang, Hsien-Yung, Jouaiti, Melanie, Zhao, Yongkun, Yu, Zhenhua, Vaidyanathan, Ravi, Farina, Dario
• Format: Journal Article
• Language: English
• Published: IEEE 12-11-2024
• Subjects: CNN; electrode loss; GRU; high-density sEMG; locomotion transition prediction
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2024.3497658

Prediction of transition between locomotion modes (e.g. moving from flat ground to stairs, etc) is vital for optimal interface with lower limb assistive technologies such as exoskeletons and prostheses. Inertial and bipolar electromyography (EMG) sensors have been investigated, but accuracy for clinical utility remains unresolved. This shortfall may be attributed to their limited capacity to detect subtle changes in muscle activations, particularly during the early stages of locomotion transitions (e.g., near the toe-off). In this study, we examined the effectiveness of two high-density surface electromyography (HDsEMG) sensors in detecting muscle activation changes during stair-related transitions. The results revealed that compared to bipolar EMG on the same muscles, HDsEMG-based methods increased transition prediction accuracy significantly from 70.2% to 91.1% when predicting at toe-off and from 89.8% to 99.2% when predicting with a delay of 400-ms relative to toe-off. This demonstrated the superior ability of HDsEMG to capture subtle muscle activation changes, especially during early transition stages. We also found reducing the electrode count to 21 per muscle only minimally impacted performance (88.3% accuracy at toe-off). This suggests distributing the same total number of electrodes across more muscles could potentially further improve prediction accuracy without increasing computational load. Moreover, by implementing image-inpainting signal processing, HDsEMG demonstrated robustness against the common issue of electrode signal loss. Even with 30% electrode detachment, prediction accuracy decreased only by 3%. We argue that HDsEMG offers a promising solution to bridge the gap in locomotion transition prediction for interface with assistive technology.