Department of Kinesiology, Pennsylvania State University, University Park, PA, 16802, USA.
Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA.
Sci Rep. 2022 May 30;12(1):8993. doi: 10.1038/s41598-022-11966-3.
Walking humans display great versatility when achieving task goals, like avoiding obstacles or walking alongside others, but the relevance of this to fall avoidance remains unknown. We recently demonstrated a functional connection between the motor regulation needed to achieve task goals (e.g., maintaining walking speed) and a simple walker's ability to reject large disturbances. Here, for the same model, we identify the viability kernel-the largest state-space region where the walker can step forever via at least one sequence of push-off inputs per state. We further find that only a few basins of attraction of the speed-regulated walker's steady-state gaits can fully cover the viability kernel. This highlights a potentially important role of task-level motor regulation in fall avoidance. Therefore, we posit an adaptive hierarchical control/regulation strategy that switches between different task-level regulators to avoid falls. Our task switching controller only requires a target value of the regulated observable-a "task switch"-at every walking step, each chosen from a small, predetermined collection. Because humans have typically already learned to perform such goal-directed tasks during nominal walking conditions, this suggests that the "information cost" of biologically implementing such controllers for the nervous system, including cognitive demands in humans, could be quite low.
行走的人类在实现任务目标时表现出很大的灵活性，例如避开障碍物或与他人并肩行走，但这对避免跌倒的相关性尚不清楚。我们最近证明了运动调节所需的功能连接与简单步行者拒绝大干扰的能力之间存在联系。在这里，对于同一个模型，我们确定了可行核——在每个状态下通过至少一次推离输入，步行者可以永远踏步的最大状态空间区域。我们进一步发现，只有速度调节步行者稳态步态的几个吸引盆地才能完全覆盖可行核。这突出了任务级运动调节在避免跌倒方面的潜在重要作用。因此，我们提出了一种自适应分层控制/调节策略，该策略在不同的任务级调节器之间切换以避免跌倒。我们的任务切换控制器只需要在每个步行步骤中都有被调节的可观察值的目标值——一个“任务切换”，每个目标值都选自一个小的、预先确定的集合。因为人类在正常行走条件下通常已经学会执行此类目标导向任务，所以这表明对于神经系统，包括人类的认知需求，实现此类控制器的“信息成本”可能相当低。
