1. Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning
- Author
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Cheng-Wu Lin, Shanq-Jang Ruan, Wei-Chun Hsu, Ya-Wen Tu, and Shao-Li Han
- Subjects
plantar pressure ,center of pressure ,sensor placement optimization ,deep reinforcement learning ,soft actor–critic discrete ,Chemical technology ,TP1-1185 - Abstract
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.
- Published
- 2020
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