Repetitive Lifting Motion Predictions Considering Muscle Fatigue

Abstract

This paper predicts the optimal motion for a repetitive lifting task considering muscle fatigue. The Denavit–Hartenberg (DH) representation is employed to characterize the two-dimensional (2D) digital human model with 10 degrees-of-freedom (DOFs). Two joint-based muscle fatigue models, i.e., a three-compartment controller (3CC) muscle fatigue model (validated for isometric tasks) and a four-compartment controller with augmented recovery (4CCr) muscle fatigue model (validated for dynamic tasks), are utilized to account for the fatigue effect due to the repetitive motion. The lifting problem is formulated mathematically as an optimization problem, with the objective of minimizing dynamic effort and joint acceleration subjected to both physical and task-specific constraints. The design variables include joint angle profiles, discretized by quartic B-splines, and the control points of the profiles of the fatigue compartments associated with major body joints (spinal, shoulder, elbow, hip, and knee joints). The outcomes of the simulation encompass profiles of joint angles, joint torques, and the advancement of joint fatigue. It is notable that the profiles of joint angles and torques exhibit distinct periodic patterns. Numerical simulations and experiments with a 20 kg box reveal that the maximum predicted lifting cycles are 11 for the 3CC fatigue model and 13 for the 4CCr fatigue model while the experimental result is 13 cycles. The results indicate that the 4CCr muscle fatigue model provides enhanced accuracy over the 3CC model for predicting task duration (number of cycles) of repetitive lifting.