Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models

Sensors (Basel). 2022 Apr 30;22(9):3442. doi: 10.3390/s22093442.

Abstract

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body's center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants' feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs.

Keywords: force sensing resistor sensors; heel height; high-heeled shoes; plantar pressure; regression model.

MeSH terms

  • Biomechanical Phenomena
  • Female
  • Foot
  • Heel*
  • Humans
  • Shoes
  • Walking*

Grants and funding

This research was partly supported by the Ministry of Science and Technology in Taiwan, under grants MOST 109-2221-E-305-001-MY2 and MOST 110-2314-B-305-001. This research was also partly supported by the University System of Taipei Joint Research Program, under grant USTP-NTPU-TMU-109-03 and USTP-NTPU-NTOU-110-01, Faculty Group Research Funding Sponsorship by National Taipei University, under grant 2021-NTPU-ORDA-02, and the “Academic Top-Notch and Features Field Project” Funding Sponsorship of National Taipei University, Taiwan, under grant 110-NTPU_ORDA-F-003.