John Paul D. Serrano, Jamie Mitchelle A. Soltez, Rodney Karlo C. Pascual, John Christopher D. Castillo, J. L. Torres, F. Cruz
{"title":"基于皮肤电反应、心率和体温的便携式压力水平检测器","authors":"John Paul D. Serrano, Jamie Mitchelle A. Soltez, Rodney Karlo C. Pascual, John Christopher D. Castillo, J. L. Torres, F. Cruz","doi":"10.1109/HNICEM.2018.8666352","DOIUrl":null,"url":null,"abstract":"A portable device was designed and developed that measures stress level based from physiological signals gathered from a person. The device uses three physiological signals, the galvanic skin response (GSR), heart rate (HR), and body temperature (BT). The system uses a microcomputer to fetch and process the data, and to compute the stress level using the applied machine learning algorithm. From the results, it was verified that the system provides accurate measurements of the physiological signals with an average percent difference of 0.68338% and 0.19327% for HR and BT respectively.","PeriodicalId":426103,"journal":{"name":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Portable Stress Level Detector based on Galvanic Skin Response, Heart Rate, and Body Temperature\",\"authors\":\"John Paul D. Serrano, Jamie Mitchelle A. Soltez, Rodney Karlo C. Pascual, John Christopher D. Castillo, J. L. Torres, F. Cruz\",\"doi\":\"10.1109/HNICEM.2018.8666352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A portable device was designed and developed that measures stress level based from physiological signals gathered from a person. The device uses three physiological signals, the galvanic skin response (GSR), heart rate (HR), and body temperature (BT). The system uses a microcomputer to fetch and process the data, and to compute the stress level using the applied machine learning algorithm. From the results, it was verified that the system provides accurate measurements of the physiological signals with an average percent difference of 0.68338% and 0.19327% for HR and BT respectively.\",\"PeriodicalId\":426103,\"journal\":{\"name\":\"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HNICEM.2018.8666352\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HNICEM.2018.8666352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Portable Stress Level Detector based on Galvanic Skin Response, Heart Rate, and Body Temperature
A portable device was designed and developed that measures stress level based from physiological signals gathered from a person. The device uses three physiological signals, the galvanic skin response (GSR), heart rate (HR), and body temperature (BT). The system uses a microcomputer to fetch and process the data, and to compute the stress level using the applied machine learning algorithm. From the results, it was verified that the system provides accurate measurements of the physiological signals with an average percent difference of 0.68338% and 0.19327% for HR and BT respectively.