Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/9/3442 |
| _version_ | 1797502798777024512 |
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| author | Yi-Ting Hwang Si-Huei Lee Bor-Shing Lin |
| author_facet | Yi-Ting Hwang Si-Huei Lee Bor-Shing Lin |
| author_sort | Yi-Ting Hwang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:41:17Z |
| format | Article |
| id | doaj.art-a93c83b7a17042e19f35fa3c9230278f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:41:17Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a93c83b7a17042e19f35fa3c9230278f2023-11-23T09:18:32ZengMDPI AGSensors1424-82202022-04-01229344210.3390/s22093442Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression ModelsYi-Ting Hwang0Si-Huei Lee1Bor-Shing Lin2Department of Statistics, National Taipei University, New Taipei City 237303, TaiwanDepartment of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei 112, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, TaiwanWomen 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.https://www.mdpi.com/1424-8220/22/9/3442high-heeled shoesplantar pressureforce sensing resistor sensorsregression modelheel height |
| spellingShingle | Yi-Ting Hwang Si-Huei Lee Bor-Shing Lin Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models Sensors high-heeled shoes plantar pressure force sensing resistor sensors regression model heel height |
| title | Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models |
| title_full | Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models |
| title_fullStr | Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models |
| title_full_unstemmed | Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models |
| title_short | Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models |
| title_sort | assessment system for predicting maximal safe range for heel height by using force sensing resistor sensors and regression models |
| topic | high-heeled shoes plantar pressure force sensing resistor sensors regression model heel height |
| url | https://www.mdpi.com/1424-8220/22/9/3442 |
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