Wearables-Only Analysis of Muscle and Joint Mechanics: An EMG-Driven Approach.

ABSTRACT: Complex sensor arrays prohibit practical deployment of existing wearables-based algorithms for free-living analysis of muscle and joint mechanics. Machine learning techniques have been proposed as a potential solution, however, they are less interpretable and generalizable when compared to physics-based techniques. Herein, we propose a hybrid method utilizing inertial sensor- and electromyography (EMG)-driven simulation of muscle contraction to characterize knee joint and muscle mechanics during walking gait. Machine learning is used only to map a subset of measured muscle excitations to a full set thereby reducing the number of required sensors. We demonstrate the utility of the approach for estimating net knee flexion moment (KFM) as well as individual muscle moment and work during the s

SUBMITTER: Gurchiek RD 

PROVIDER: S-EPMC8820126 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature