Session: CIE-22-01 - AMS-CAPPD: Digital Twin: Advanced Human Modeling and Simulation in Engineering

Paper Number: 89127

89127 - Hybrid Predictive Model for Assessing Spinal Loads for 3d Asymmetric Lifting 

In this study, a hybrid predictive model is used to predict 3D asymmetric lifting motion and assess potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics based optimization method. The equations of motion are built by recursive Lagrangian dynamics. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the generated kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool. Muscle activation results between simulated and experimental EMG are compared to validate the model. Finally, potential lower back injuries are evaluated for a specific-weight asymmetric lifting task. The shear and compression spine loads are compared to NIOSH recommended limits. At the beginning of the dynamic lifting process, the simulated compressive spine load beyonds the NIOSH action limit but less than the permissible limit. This is due to the fatigue factors considered in NIOSH lifting equation.

Presenting Author: Yujiang Xiang Oklahoma State University

Presenting Author Biography: Dr. Xiang is an assistant professor in the Department of Mechanical and Aerospace Engineering at Oklahoma State University.

Authors:

Rahid Zaman Oklahoma State University
Joel Quarnstrom Oklahoma State University
Yujiang Xiang Oklahoma State University
Ritwik Rakshit Texas Tech University
James Yang Texas Tech University