{"title":"A new illness recognition framework using frequent temporal pattern mining","authors":"Z. Hajihashemi, M. Popescu","doi":"10.1145/2638728.2638805","DOIUrl":null,"url":null,"abstract":"Living alone in their own residence, older adults are at-risk for late assessment of physical or cognitive changes due to many factors such as their impression that such changes are simply a normal part of aging or their reluctance to admit to a problem. Sensors networks have emerged in the last decade as a possible solution to older adult health monitoring and early illness recognition. Typical early illness recognition approaches are either concentrated on the detection of a given set of activities such as a fall or walks, or on the detection of anomalies such as too many bathroom visits. In this paper we propose a new illness recognition framework, MFA, based on detecting a missing frequent activity from the daily routine. MFA is implemented using a frequent temporal pattern detection algorithm and demonstrated on a pilot dataset collected in TigerPlace, an aging in place community from Columbia, Missouri.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2638728.2638805","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Living alone in their own residence, older adults are at-risk for late assessment of physical or cognitive changes due to many factors such as their impression that such changes are simply a normal part of aging or their reluctance to admit to a problem. Sensors networks have emerged in the last decade as a possible solution to older adult health monitoring and early illness recognition. Typical early illness recognition approaches are either concentrated on the detection of a given set of activities such as a fall or walks, or on the detection of anomalies such as too many bathroom visits. In this paper we propose a new illness recognition framework, MFA, based on detecting a missing frequent activity from the daily routine. MFA is implemented using a frequent temporal pattern detection algorithm and demonstrated on a pilot dataset collected in TigerPlace, an aging in place community from Columbia, Missouri.