INSIGHTS INTO HUMAN LOCOMOTION STRATEGIES AND MOTOR LEARNING FOR AN AGEING POPULATION USING TRANSFER TESTING AND COUPLED SIMULATIONS

Abstract

The elucidation of human locomotion strategies has potential applications in the prevention of sarcopenia and in the reduction of falls. Given the diverse biochemical, mechanical and functional age-related changes seen in the neuro-musculoskeletal system, the decline in motor function is difficult to study experimentally. In this study, we use transfer testing and coupled simulation strategies within a deep reinforcement learning environment to better understand the complex problem of motor control adaptation to age-related changes. Using transfer testing, a 3D musculoskeletal model is separately trained on parameters of the young adult model (Y) for either forward or backward falls after completing two steps forward, and tested using a 30% age-related reduction for all parameters (M_all). This strategy produces a backward fall for a forwardly trained simulation, showing potential sensitivity of these parameters to a given fall direction. Second, a coupled simulation solution is used to simulate recovery from falls by considering the center-of-mass position relative to the base of support. Results for the M_all trained model showed a longer simulation time and a greater vertical pelvis velocity with a maximal value of 4.26$$m/s. In particular, the results of the coupled simulations clearly show that both the young and M_all condition models respond with a step back and stronger leg extensor activations to propel the model forward to recover from the simulated fall. We developed a novel coupling between transfer testing and coupled simulation strategies to improve upon muscle models for characterizing muscle function, and also to begin testing different hypotheses, such as the strategy and force required to avoid a fall at different limits. This opens new avenues for precision rehabilitation with patient-specific muscle-driven recovery exercises.