Development of Plantar Pressure Measurement System and Personal Classification Study based on Plantar Pressure Image

• Jong Gab Ho,
• Dae Gyeom Kim,
• Young Kim,
• Seung-wan Jang,
• Se Dong Min,

Vol. 15, No. 11, November 30, 2021
10.3837/tiis.2021.11.001, Download Paper (Free):

• Convolutional Neural Network
• Monitoring application
• Plantar pressure image
• Plantar pressure index
• Velostat pressure sensor

Abstract

In this study, a Velostat pressure sensor was manufactured to develop a plantar pressure measurement system and a C#-based application was developed to monitor and collect plantar pressure data in real time. In order to evaluate the characteristics of the proposed plantar pressure measurement system, the accuracy of plantar pressure index and personal classification was verified by comparing with MatScan, a commercial plantar pressure measurement system. As a result, the output characteristics according to the weight of the Velostat pressure sensor were evaluated and a trend line with the reliability of r2 = 0.98 was detected. The Root Mean Square Error(RMSE) of the weighted area was 11.315 cm2, the RMSE of the x coordinate of Center of Pressure(CoPx) was 1.036 cm and the RMSE of the y coordinate of Center of Pressure(CoPy) was 0.936 cm. Finally, inaccuracy of personal classification, the proposed system was 99.47% and MatScan was 96.86%. Based on the advantage of being simple to implement and capable of manufacturing at low cost, it is considered that it can be applied to various fields of measuring vital signs such as sitting posture and breathing in addition to the plantar pressure measurement system.

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