{"title":"用于检测痴呆患者夜间躁动的穿戴式监测","authors":"A. C. Marcén, J. Carro, V. Monasterio","doi":"10.5220/0005938500630069","DOIUrl":null,"url":null,"abstract":"Nocturnal agitation is one of the symptoms exhibited by dementia patients. Diagnosing and monitoring the \n \nevolution of agitation is difficult because patient monitoring requires a doctor, nurse or caregiver observing \n \npatients for extended periods of time. In this work, we propose to use an automatic monitoring system based \n \non wearable technology that complements the caregiverâs work. The proposed system uses a wrist wearable \n \ndevice to record agitation data, which are subsequently classified through machine learning techniques as \n \nquantifiable indexes of nocturnal agitation. Preliminary tests performed with volunteers showed that the classification \n \nof recorded movements between nocturnal agitation or quiet periods was successful in 78.86% of the \n \ncases. This proof of concept demonstrates the feasibility of using wearable technology to monitor nocturnal \n \nagitation.","PeriodicalId":298357,"journal":{"name":"International Conference on Pervasive and Embedded Computing and Communication Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Wearable Monitoring for the Detection of Nocturnal Agitation in Dementia\",\"authors\":\"A. C. Marcén, J. Carro, V. Monasterio\",\"doi\":\"10.5220/0005938500630069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nocturnal agitation is one of the symptoms exhibited by dementia patients. Diagnosing and monitoring the \\n \\nevolution of agitation is difficult because patient monitoring requires a doctor, nurse or caregiver observing \\n \\npatients for extended periods of time. In this work, we propose to use an automatic monitoring system based \\n \\non wearable technology that complements the caregiverâs work. The proposed system uses a wrist wearable \\n \\ndevice to record agitation data, which are subsequently classified through machine learning techniques as \\n \\nquantifiable indexes of nocturnal agitation. Preliminary tests performed with volunteers showed that the classification \\n \\nof recorded movements between nocturnal agitation or quiet periods was successful in 78.86% of the \\n \\ncases. This proof of concept demonstrates the feasibility of using wearable technology to monitor nocturnal \\n \\nagitation.\",\"PeriodicalId\":298357,\"journal\":{\"name\":\"International Conference on Pervasive and Embedded Computing and Communication Systems\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Pervasive and Embedded Computing and Communication Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0005938500630069\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Pervasive and Embedded Computing and Communication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0005938500630069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wearable Monitoring for the Detection of Nocturnal Agitation in Dementia
Nocturnal agitation is one of the symptoms exhibited by dementia patients. Diagnosing and monitoring the
evolution of agitation is difficult because patient monitoring requires a doctor, nurse or caregiver observing
patients for extended periods of time. In this work, we propose to use an automatic monitoring system based
on wearable technology that complements the caregiverâs work. The proposed system uses a wrist wearable
device to record agitation data, which are subsequently classified through machine learning techniques as
quantifiable indexes of nocturnal agitation. Preliminary tests performed with volunteers showed that the classification
of recorded movements between nocturnal agitation or quiet periods was successful in 78.86% of the
cases. This proof of concept demonstrates the feasibility of using wearable technology to monitor nocturnal
agitation.