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A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications

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  • Li-Wei Chou

    (Department of Physical Medicine and Rehabilitation, China Medical University Hospital, Taichung City 40402, Taiwan
    Department of Physical Therapy, Graduate Institute of Rehabilitation Science, China Medical University, Taichung City 40402, Taiwan
    Department of Physical Medicine and Rehabilitation, Asia University Hospital, Taichung City 41354, Taiwan)

  • Jun-Hong Shen

    (Department of Information Communication, Asia University, Taichung City 41354, Taiwan
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City 40447, Taiwan)

  • Hui-Ting Lin

    (Department of Physical Therapy, I-Shou University, Kaohsiung City 82445, Taiwan)

  • Yi-Tung Yang

    (Department of Physical Medicine and Rehabilitation, Asia University Hospital, Taichung City 41354, Taiwan)

  • Wen-Pin Hu

    (Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City 40447, Taiwan
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung City 41354, Taiwan)

Abstract

The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, we investigate the potential applications of insoles with fewer sensing spots and the consequent reduction in the amount of data acquired from both feet. The main purpose is to discuss the influence of the layout of these pressure sensing points of the insole design on the center of pressure (COP) calculation. The insole used in this study has 89 pressure sensing spots, and we used data from 36, 29, 20, and 11 sensing points in simplified calculation types. Among these four simplified calculation types, Type 1 exhibited the best accuracy of the COP calculation, and Type 4 obtained the worst results. Type 2 and Type 3 exhibited inferior accuracy of the COP calculation, but they still sufficed for applications that did not require high accuracy. Aside from the factor of the number of sensing spots used in the calculation, we also demonstrated that the location of selected sensors could influence the accuracy of COP calculation in the analyses by using the different combinations of metatarsal areas and other areas (heel, central, lateral toes, and hallux). The results of this research could be a reference for making a simplified form of pressure sensing Internet-of-Health Things (IoHT) insole with a reduced product cost.

Suggested Citation

  • Li-Wei Chou & Jun-Hong Shen & Hui-Ting Lin & Yi-Tung Yang & Wen-Pin Hu, 2021. "A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2934-:d:513084
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    References listed on IDEAS

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    1. Yanning Dai & Yuedong Xie & Junliang Chen & Shuaibo Kang & Lijun Xu & Shuo Gao, 2020. "A lamination-based piezoelectric insole gait analysis system for massive production for Internet-of-health things," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
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