A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks.

ABSTRACT: This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder-elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user's movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm r

SUBMITTER: Penna MF 

PROVIDER: S-EPMC11579892 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature