Laavanya Rachakonda, P. Sundaravadivel, S. Mohanty, E. Kougianos, M. Ganapathiraju
{"title":"IoMT中用于应力水平检测的智能传感器","authors":"Laavanya Rachakonda, P. Sundaravadivel, S. Mohanty, E. Kougianos, M. Ganapathiraju","doi":"10.1109/ISES.2018.00039","DOIUrl":null,"url":null,"abstract":"Psychological stress is a sense of pressure which affects the physiological parameters in a person. In this paper a novel stress detection system, iStress is proposed which monitors stress levels through body temperature, rate of motion and sweat during physical activity. The implementation of the iStress system uses a neural network approach utilizing a Mamdani-type fuzzy logic controller with more than 150 instances as the model. The collected data are sent and stored in the cloud, which can help in real time monitoring of the person's stress level thereby reducing risks to health. This system consumes low energy although operating in real time. The proposed system has an ability to produce results with 97% accuracy, low system complexity and moderate cost.","PeriodicalId":447663,"journal":{"name":"2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"A Smart Sensor in the IoMT for Stress Level Detection\",\"authors\":\"Laavanya Rachakonda, P. Sundaravadivel, S. Mohanty, E. Kougianos, M. Ganapathiraju\",\"doi\":\"10.1109/ISES.2018.00039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Psychological stress is a sense of pressure which affects the physiological parameters in a person. In this paper a novel stress detection system, iStress is proposed which monitors stress levels through body temperature, rate of motion and sweat during physical activity. The implementation of the iStress system uses a neural network approach utilizing a Mamdani-type fuzzy logic controller with more than 150 instances as the model. The collected data are sent and stored in the cloud, which can help in real time monitoring of the person's stress level thereby reducing risks to health. This system consumes low energy although operating in real time. The proposed system has an ability to produce results with 97% accuracy, low system complexity and moderate cost.\",\"PeriodicalId\":447663,\"journal\":{\"name\":\"2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISES.2018.00039\",\"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 International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISES.2018.00039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Smart Sensor in the IoMT for Stress Level Detection
Psychological stress is a sense of pressure which affects the physiological parameters in a person. In this paper a novel stress detection system, iStress is proposed which monitors stress levels through body temperature, rate of motion and sweat during physical activity. The implementation of the iStress system uses a neural network approach utilizing a Mamdani-type fuzzy logic controller with more than 150 instances as the model. The collected data are sent and stored in the cloud, which can help in real time monitoring of the person's stress level thereby reducing risks to health. This system consumes low energy although operating in real time. The proposed system has an ability to produce results with 97% accuracy, low system complexity and moderate cost.