Dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation: Model development and validation

Abstract

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.

Single Task Optimization-Based Planar Box Delivery Motion Simulation and Experimental Validation

“…Therefore, joint torque limits are important for predicting potential injuries for workers. This has been demonstrated for the maximum weight lifting prediction by considering dynamic joint strength [36].…”

Three-dimensional asymmetric maximum weight lifting prediction considering dynamic joint strength

“…The optimal lifting time is 1.33 s. The strength ( z score ) for the simulated model was retrieved from symmetric maximum weight lifting as 1.05. 14,24 Figure 3 depicts the predicted joint angle and vertical GRF profiles. The snapshots of the optimal asymmetric lifting motion are shown in Figure 4.…”

“…However, most of the studies focus on symmetric lifting. 9–14 Researches on asymmetric lifting prediction are very few, and most of them are based on static lifting. 15 Maximum lifting weight prediction requires the dynamic strength in joint space.…”

Hybrid musculoskeletal model-based 3D asymmetric lifting prediction and comparison with symmetric lifting

“…24 The simplest biomechanical models are 2D models, which are computationally efficient. 20,24,[29][30][31][32] The National Institute of Occupational Safety and Health (NIOSH) proposed a lifting equation which later became a standard for evaluating a lifting task in commercial workspaces. 33,34 This is a regression-based 2D model and does not contain any muscle physiology and spinal shear stress in its evaluation procedure.…”