Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds.

ABSTRACT:

Background

Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI's prediction accuracy.

Objecti

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC9361708 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature