Deep Learning Models Optimization for Gait Phase Identification from EMG Data During Exoskeleton-Assisted Walking.

ABSTRACT: Exoskeletons are a fast-growing technology that enables multiple use-cases in clinical scenarios. They can be useful tools for the rehabilitation of patients with motor dysfunctions caused by neurological conditions, aging or trauma. Assistive exoskeletons modulate the torque exerted by the electrical motors moving their joints to allow the patients wearing them to achieve an intended movement, such as gait, correctly. Their effectiveness, therefore, requires accurate online control of such torques to complement those generated by the patient. Hereby we explored Deep Learning (DL) models to generate an online prediction of the gait phase, i.e., stance or swing, during assisted walking with a lower-limb exoskeleton based on surface electromyography (sEMG) data. We leveraged the lead of musc

SUBMITTER: Soldi R 

PROVIDER: S-EPMC12467064 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature