Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
Sensors (Basel). 2020 Sep 29;20(19):5588. doi: 10.3390/s20195588.
We study the foot plantar sensor placement by a deep reinforcement learning algorithm without using any prior knowledge of the foot anatomical area. To apply a reinforcement learning algorithm, we propose a sensor placement environment and reward system that aims to optimize fitting the center of pressure (COP) trajectory during the self-selected speed running task. In this environment, the agent considers placing eight sensors within a 7 × 20 grid coordinate system, and then the final pattern becomes the result of sensor placement. Our results show that this method (1) can generate a sensor placement, which has a low mean square error in fitting ground truth COP trajectory, and (2) robustly discovers the optimal sensor placement in a large number of combinations, which is more than 116 quadrillion. This method is also feasible for solving different tasks, regardless of the self-selected speed running task.
我们通过深度强化学习算法研究足底传感器的放置位置，而无需使用任何足部解剖区域的先验知识。为了应用强化学习算法，我们提出了一种传感器放置环境和奖励系统，旨在优化在自选择速度跑步任务期间拟合中心压力 (COP) 轨迹。在这个环境中，代理考虑在 7×20 网格坐标系内放置八个传感器，然后最终模式成为传感器放置的结果。我们的结果表明，这种方法 (1) 可以生成传感器放置，其在拟合地面真实 COP 轨迹方面具有低均方误差，并且 (2) 在大量组合中稳健地发现最佳传感器放置，数量超过 116 亿亿。这种方法也适用于解决不同的任务，而与自选择速度跑步任务无关。
