Krasimir Tonchev, Strahil Sokolov, Yuliyan Velchev, Georgi R. Balabanov, V. Poulkov
{"title":"Recognition of Human daily activities","authors":"Krasimir Tonchev, Strahil Sokolov, Yuliyan Velchev, Georgi R. Balabanov, V. Poulkov","doi":"10.1109/ICCW.2015.7247193","DOIUrl":null,"url":null,"abstract":"Capturing the type of physical activity a person is performing thorough his daily life, can inspire the development of new and innovative applications. Examples include monitoring patients' health and physical activity performance, reasoning upon the observed activity to recommend better training strategy, new therapeutic programs, etc. In this work we propose an algorithm for Human Activity Recognition based on the application of a geometrically motivated feature selection method. We test the algorithm on a standard data set and validate its performance by comparing it with the existing results of other known algorithms.","PeriodicalId":6464,"journal":{"name":"2015 IEEE International Conference on Communication Workshop (ICCW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Communication Workshop (ICCW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCW.2015.7247193","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
Capturing the type of physical activity a person is performing thorough his daily life, can inspire the development of new and innovative applications. Examples include monitoring patients' health and physical activity performance, reasoning upon the observed activity to recommend better training strategy, new therapeutic programs, etc. In this work we propose an algorithm for Human Activity Recognition based on the application of a geometrically motivated feature selection method. We test the algorithm on a standard data set and validate its performance by comparing it with the existing results of other known algorithms.
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