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This paper describes an integrated approach to predict human leg and spine muscle forces during lifting by integration of a predictive skeletal model with OpenSim. The two-dimensional (2D) skeletal lifting motion is first predicted by using an inverse dynamics optimization method. Then, the prediction outputs, including joint angle profiles, ground reaction forces, and center of pressure, are incorporated in OpenSim biomechanics software to analyze muscle forces for lifting. Therefore, the integrated approach has predictive capability on musculoskeletal level. By using this method, we can predict and analyze muscles forces for heavy weight lifting motion which is difficult to simulate directly using a 3D musculoskeletal model.

This article presents an optimization formulation and experimental validation of a dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation. Dynamic joint strength (the net moment capacity as a function of joint angle and angular velocity), as presented in the literature, is adopted in the optimization formulation to predict the symmetric maximum lifting weight and corresponding motion. Nineteen participants were recruited to perform a maximum-weight-box-lifting task in the laboratory, and kinetic and kinematic data including motion and ground reaction forces were collected using a motion capture system and force plates, respectively. For each individual, the predicted spine, shoulder, elbow, hip, knee, and ankle joint angles, as well as vertical and horizontal ground reaction force and box weight, were compared with the experimental data. Both root-mean-square error and Pearson’s correlation coefficient ( r) were used for the validation. The results show that the proposed two-dimensional optimization-based motion prediction formulation is able to accurately predict all joint angles, box weights, and vertical ground reaction forces, but not horizontal ground reaction forces.

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