Selvia AM AMAlANATHAN, Abdulaziz Asiri, Amer Al Ali
{"title":"Mental Health Prediction Using Artificial Intelligence- Machine Learning: Pain and Stress Detection Using Wearable Sensors and Devices—A Review","authors":"Selvia AM AMAlANATHAN, Abdulaziz Asiri, Amer Al Ali","doi":"10.37896/ymer21.08/45","DOIUrl":null,"url":null,"abstract":"Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This re- view presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin tempera- ture) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Keywords: Mental health, machine learning, pain detection; stress detection; wearable sensor; physiological signals; behavioral signals","PeriodicalId":23848,"journal":{"name":"YMER Digital","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"YMER Digital","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37896/ymer21.08/45","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This re- view presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin tempera- ture) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Keywords: Mental health, machine learning, pain detection; stress detection; wearable sensor; physiological signals; behavioral signals