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dc.contributor.authorChowdhury, AK
dc.contributor.authorFarseev, A
dc.contributor.authorChakraborty, PR
dc.contributor.authorTjondronegoro, D
dc.contributor.authorChandran, V
dc.date.accessioned2019-06-27T12:31:22Z
dc.date.available2019-06-27T12:31:22Z
dc.date.issued2018
dc.identifier.isbn9781538610305
dc.identifier.doi10.1109/LSC.2017.8268156
dc.identifier.urihttp://hdl.handle.net/10072/385145
dc.description.abstractEffective classification of physical exercises allows individuals to assess their levels of physical activity and functional ability for maintaining physical fitness and help reduce risks of chronic diseases. This paper investigates and compares classification techniques for detecting physical exercise in real-world contexts that often only supports a small training dataset. The system combines heart rate with other exercise-related features, such as distance, duration, calories, etc. The experiment uses a dataset of 40 realistic (uncontrolled) sessions from 22 individuals wearing wearable sensors while performing different exercises, including walking, aerobics, running, indoor cycling, and weight training. Based on a 5-fold cross validation approach, AdaBoost demonstrated the highest (87.25%) classification accuracy compared to other classifiers, including support vector machine, neural network, and binary decision tree when used individually. When fused together at the decision level using majority-voting techniques, these classifiers achieved higher accuracy (89.25%) than that of individual applications.
dc.publisherIEEE
dc.relation.ispartofconferencename2017 IEEE Life Sciences Conference (LSC)
dc.relation.ispartofconferencetitle2017 IEEE Life Sciences Conference, LSC 2017
dc.relation.ispartofdatefrom2017-12-13
dc.relation.ispartofdateto2017-12-15
dc.relation.ispartoflocationSydney, Australia
dc.relation.ispartofpagefrom111
dc.relation.ispartofpageto114
dc.relation.ispartofvolume2018-January
dc.subject.fieldofresearchHuman Movement and Sports Sciences
dc.subject.fieldofresearchcode1106
dc.titleAutomatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings
dc.typeConference output
dc.type.descriptionE2 - Conferences (Non Refereed)
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorTjondronegoro, Dian W.


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    Contains papers delivered by Griffith authors at national and international conferences.

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