{"title":"SVM-RBF Kernel Learning Model for Activity Recognition in Smart Home","authors":"Zhi-Wei Chou, Ying-Kai Lu, Ke-Nung Huang","doi":"10.1109/SNPD51163.2021.9704919","DOIUrl":null,"url":null,"abstract":"The world population is aging. Taiwan will have at least 20 percent of the population over 65 by 2026. Telemonitoring technology is one of the solutions used to assist elderly people live independently. We designed a SVM-RBF kernel learning model to classify activities of daily living and to analyze an individual’s daily routines and habits, typically for the elderly who live alone. One of the CASAS smart home datasets was used to train and to retest the algorithm. A non-trained dataset was also used to validate the accuracy of the algorithm. Abnormal behaviors can be detected by compared with individual’s daily activity pattern as baseline.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD51163.2021.9704919","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
The world population is aging. Taiwan will have at least 20 percent of the population over 65 by 2026. Telemonitoring technology is one of the solutions used to assist elderly people live independently. We designed a SVM-RBF kernel learning model to classify activities of daily living and to analyze an individual’s daily routines and habits, typically for the elderly who live alone. One of the CASAS smart home datasets was used to train and to retest the algorithm. A non-trained dataset was also used to validate the accuracy of the algorithm. Abnormal behaviors can be detected by compared with individual’s daily activity pattern as baseline.
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