Sit-to-stand can be defined as a set of movements that allow humans to rise from a sitting position to a bipedal standing pose. These movements, often categorized as four distinct kinematic phases, must be coordinated for assuring personal autonomy and can be compromised by ageing or physical impairments. To solve this, rehabilitation techniques and assistive devices demand proper description of the principles that lead to the correct completion of this motor task. While the muscular dynamics of the sit-to-stand task have been analysed, the underlying neural activity remains unknown and largely inaccessible for conventional measurement systems. Predictive simulations can propose motor controllers whose plausibility is evaluated through the comparison between simulated and experimental kinematics. In the present work, we modelled an array of reflexes that originate muscle activations as a function of proprioceptive and vestibular feedback. This feedback encodes torso position, displacement velocity and acceleration of a modelled human body with 7 segments, 9 degrees of freedom, and 50 actuators. We implemented two controllers: a four-phases controller where the reflex gains and composition vary depending on the kinematic phase, and a simpler two-phases controller, where three of the kinematic phases share the same reflex gains. Gains were optimized using Covariance Matrix Adaptation. The results of the simulations reveal, for both controllers, human-like sit-to-stand movement, with joint angles and muscular activity comparable to experimental data. The results obtained with the simplified two-phases controller indicate that a simple set of reflexes could be sufficient to drive this motor task.
Copyright: © 2022 Muñoz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.