Jessie R. Balbin, J. D. De Guzman, Joaquin Gerard N. Trinidad, Francis Dominic S. Yaya
{"title":"基于压力传感器矩阵足底分析的足部畸形诊断与健康风险预测","authors":"Jessie R. Balbin, J. D. De Guzman, Joaquin Gerard N. Trinidad, Francis Dominic S. Yaya","doi":"10.1109/I2CACIS52118.2021.9495907","DOIUrl":null,"url":null,"abstract":"One of the most overlooked parts of the body is the feet. Foot health can generally affect the overall health of a person if not treated well. This study utilized Support Vector Machines and an artificial neural network to determine the foot deformity of a person by using a foot plantar pressure sensor matrix called Velostat. The researchers used raspberry pi to run the GUI programmed using Python. The developed device will determine if the feet are normal, high arched, or low arched. The testing was done on 40 respondents and resulted in 95% accuracy in determining foot deformity.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Foot Deformity Determination and Health Risk Prediction Through Foot Plantar Analysis Using Pressure Sensor Matrix\",\"authors\":\"Jessie R. Balbin, J. D. De Guzman, Joaquin Gerard N. Trinidad, Francis Dominic S. Yaya\",\"doi\":\"10.1109/I2CACIS52118.2021.9495907\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most overlooked parts of the body is the feet. Foot health can generally affect the overall health of a person if not treated well. This study utilized Support Vector Machines and an artificial neural network to determine the foot deformity of a person by using a foot plantar pressure sensor matrix called Velostat. The researchers used raspberry pi to run the GUI programmed using Python. The developed device will determine if the feet are normal, high arched, or low arched. The testing was done on 40 respondents and resulted in 95% accuracy in determining foot deformity.\",\"PeriodicalId\":210770,\"journal\":{\"name\":\"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2CACIS52118.2021.9495907\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2CACIS52118.2021.9495907","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Foot Deformity Determination and Health Risk Prediction Through Foot Plantar Analysis Using Pressure Sensor Matrix
One of the most overlooked parts of the body is the feet. Foot health can generally affect the overall health of a person if not treated well. This study utilized Support Vector Machines and an artificial neural network to determine the foot deformity of a person by using a foot plantar pressure sensor matrix called Velostat. The researchers used raspberry pi to run the GUI programmed using Python. The developed device will determine if the feet are normal, high arched, or low arched. The testing was done on 40 respondents and resulted in 95% accuracy in determining foot deformity.