Shuangjiang Li, Rui Guo, Li He, Wei Gao, H. Qi, Gina P. Owens
{"title":"MoodMagician: a pervasive and unobtrusive emotion sensing system using mobile phones for improving human mental health","authors":"Shuangjiang Li, Rui Guo, Li He, Wei Gao, H. Qi, Gina P. Owens","doi":"10.1145/2668332.2668371","DOIUrl":null,"url":null,"abstract":"In this demo, we present MoodMagician, a pervasive and unobtrusive mobile phone system for inferring human emotions through the recording, processing, and analysis of the real-time streaming Galvanic Skin Response (GSR) signal from human bodies. Being different from traditional multimodal emotion sensing systems which rely on data from multiple sensing sources and may hence interfere with people's daily life, our proposed system is able to detect various categories of human emotions using single GSR signal, which is captured by compact and wearable mobile sensing devices in an unobtrusive fashion. The proposed system has been evaluated by well-designed practical experiments to recognize human emotions. The recognition accuracy of each emotion can be up to 70% through the development of effective preprocessing algorithms and the extraction of representative features from the GSR signals.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2668332.2668371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
In this demo, we present MoodMagician, a pervasive and unobtrusive mobile phone system for inferring human emotions through the recording, processing, and analysis of the real-time streaming Galvanic Skin Response (GSR) signal from human bodies. Being different from traditional multimodal emotion sensing systems which rely on data from multiple sensing sources and may hence interfere with people's daily life, our proposed system is able to detect various categories of human emotions using single GSR signal, which is captured by compact and wearable mobile sensing devices in an unobtrusive fashion. The proposed system has been evaluated by well-designed practical experiments to recognize human emotions. The recognition accuracy of each emotion can be up to 70% through the development of effective preprocessing algorithms and the extraction of representative features from the GSR signals.