Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning.

ABSTRACT: Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment's center of mass (COM), relative to a fixed

SUBMITTER: Boukhennoufa I 

PROVIDER: S-EPMC9133596 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature