{"title":"Eating activity primitives detection - a step towards ADL recognition","authors":"A. Tolstikov, J. Biswas, C. Tham, P. Yap","doi":"10.1109/HEALTH.2008.4600106","DOIUrl":null,"url":null,"abstract":"Activity of daily living (ADL) monitoring is important in order to determine the well being of elderly persons in their home settings. One important question is, ldquoIs the elderly person able to eat properly on his own?rdquo In this paper we present some results of our preliminary work on an algorithm for detection of the eating activity. The algorithm uses a dynamic Bayesian network based approach to reduce the complexity of determining states. Initial results are quite promising and point to a general algorithmic approach that a) uses multiple modalities of sensors for gathering data, b) detects activity primitives and c) stores detected activity primitives as micro-context for future use.","PeriodicalId":193623,"journal":{"name":"HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HEALTH.2008.4600106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38
Abstract
Activity of daily living (ADL) monitoring is important in order to determine the well being of elderly persons in their home settings. One important question is, ldquoIs the elderly person able to eat properly on his own?rdquo In this paper we present some results of our preliminary work on an algorithm for detection of the eating activity. The algorithm uses a dynamic Bayesian network based approach to reduce the complexity of determining states. Initial results are quite promising and point to a general algorithmic approach that a) uses multiple modalities of sensors for gathering data, b) detects activity primitives and c) stores detected activity primitives as micro-context for future use.