Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction With an OpenSim Musculoskeletal Model

Objective: In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities. Methods: First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out. Results: Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model. Conclusion: The proposed hybrid model combines the skeletal model's rapid motion prediction with OpenSim's complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.