Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.

ABSTRACT: Portable measurement systems using inertial sensors enable motion capture outside the lab, facilitating longitudinal and large-scale studies in natural environments. However, estimating 3D kinematics and kinetics from inertial data for a comprehensive biomechanical movement analysis is still challenging. Machine learning models or stepwise approaches performing Kalman filtering, inverse kinematics, and inverse dynamics can lead to inconsistencies between kinematics and kinetics. We investigated the reconstruction of 3D kinematics and kinetics of arbitrary running motions from inertial sensor data using optimal control simulations of full-body musculoskeletal models. To evaluate the feasibility of the proposed method, we used marker tracking simulations created from optical motion capture d

SUBMITTER: Nitschke M 

PROVIDER: S-EPMC11018991 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature