Activity classification using a single wrist-worn accelerometer

Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placemen...

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Main Authors: Chernbumroong S., Atkins A.S., Yu H.
Format: Conference or Workshop Item
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-84255177530&partnerID=40&md5=3693c61c43215d1b7afc5a769a38a0b7
http://cmuir.cmu.ac.th/handle/6653943832/964
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-9642014-08-29T09:09:59Z Activity classification using a single wrist-worn accelerometer Chernbumroong S. Atkins A.S. Yu H. Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor. © 2011 IEEE. 2014-08-29T09:09:59Z 2014-08-29T09:09:59Z 2011 Conference Paper 9.78147E+12 10.1109/SKIMA.2011.6089975 87819 http://www.scopus.com/inward/record.url?eid=2-s2.0-84255177530&partnerID=40&md5=3693c61c43215d1b7afc5a769a38a0b7 http://cmuir.cmu.ac.th/handle/6653943832/964 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor. © 2011 IEEE.
format Conference or Workshop Item
author Chernbumroong S.
Atkins A.S.
Yu H.
spellingShingle Chernbumroong S.
Atkins A.S.
Yu H.
Activity classification using a single wrist-worn accelerometer
author_facet Chernbumroong S.
Atkins A.S.
Yu H.
author_sort Chernbumroong S.
title Activity classification using a single wrist-worn accelerometer
title_short Activity classification using a single wrist-worn accelerometer
title_full Activity classification using a single wrist-worn accelerometer
title_fullStr Activity classification using a single wrist-worn accelerometer
title_full_unstemmed Activity classification using a single wrist-worn accelerometer
title_sort activity classification using a single wrist-worn accelerometer
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-84255177530&partnerID=40&md5=3693c61c43215d1b7afc5a769a38a0b7
http://cmuir.cmu.ac.th/handle/6653943832/964
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