Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology - Tang, Biwei; Peng, Yaling; Luo, Jing; Zhou, Yaqian; Pang, Muye; Xiang, Kui (Frontiers in Bioengineering and Biotechnology, 2022)
Investigating the optimal control strategy involved in human lifting motion can provide meritorious insights on designing and controlling wearable robotic devices to release human low-back pain and fatigue. However, determining the latent cost function regarding to this motion remains challenging due to complexities of human central nervous system. Recently, it has been discovered that the underlying cost function of a biological motion can be identified from an inverse optimization control (IOC) issue which could be handled via the bilevel optimization technology. Inspired by this discovery, this paper dedicates to studying the underlying cost function of human lifting task through the bilevel optimization technology. To this end, a nested bilevel optimization approach is developed red by integrating particle swarm optimization (PSO) with the direction collocation (DC) method. The upper level optimizer leverages PSO to optimize weighting parameters among different predefined performance criteria in the cost function, while minimizing the kinematic error between the experimental data and the result predicted from the lower-level optimizer. The lower-level optimizer implements the DC method to predict human kinematic and dynamic information based on the human musculoskeletal model inserted into OpenSim. Followed after a benchmark study, the developed method is then evaluated by experimental tests over different subjects. The experimental results reveal that the proposed method is effective at finding the cost function of human lifting task. Thus, the proposed method could be regarded as a paramount alternative in the predictive simulation of human lifting motion.