Inverse optimal control with time-varying objectives: application to human jumping movement analysis

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

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 this motion remains challenging due to the complexities of the 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 can be handled via the bilevel optimization technology. Inspired by this discovery, this work is dedicated to studying the underlying cost function of human lifting tasks through the bilevel optimization technology. To this end, a nested bilevel optimization approach is developed by integrating particle swarm optimization (PSO) with the direction collocation (DC) method. The upper level optimizer leverages particle swarm optimization 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 by the lower level optimizer. The lower level optimizer implements the direction collocation method to predict human kinematic and dynamic information based on the human musculoskeletal model inserted into OpenSim. Following after a benchmark study, the developed method is evaluated by experimental tests on different subjects. The experimental results reveal that the proposed method is effective at finding the cost function of human lifting tasks. Thus, the proposed method could be regarded as a paramount alternative in the predictive simulation of human lifting motion.

Implications of Optimal Feedback Control Theory for Sport Coaching and Motor Learning: A Systematic Review

Best practice in skill acquisition has been informed by motor control theories. The main aim of this study is to screen existing literature on a relatively novel theory, Optimal Feedback Control Theory (OFCT), and to assess how OFCT concepts can be applied in sports and motor learning research. Based on 51 included studies with on average a high methodological quality, we found that different types of training seem to appeal to different control processes within OFCT. The minimum intervention principle (founded in OFCT) was used in many of the reviewed studies, and further investigation might lead to further improvements in sport skill acquisition. However, considering the homogenous nature of the tasks included in the reviewed studies, these ideas and their generalizability should be tested in future studies.