Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning

Cheng-Wu Lin; Shanq-Jang Ruan; Wei-Chun Hsu; Ya-Wen Tu; Shao-Li Han

doi:10.3390/s20195588

Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning

Sensors (Basel). 2020 Sep 29;20(19):5588. doi: 10.3390/s20195588.

Authors

Cheng-Wu Lin¹, Shanq-Jang Ruan¹, Wei-Chun Hsu², Ya-Wen Tu³, Shao-Li Han³

Affiliations

¹ Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
² Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
³ Sijhih Cathay General Hospital, New Taipei 221, Taiwan.

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.

Keywords: center of pressure; deep reinforcement learning; plantar pressure; sensor placement optimization; soft actor–critic discrete.

Publication types

Letter