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dc.contributor.authorSaisakul Chernbumroongen_US
dc.contributor.authorAnthony S. Atkinsen_US
dc.contributor.authorHongnian Yuen_US
dc.description.abstractAutomatic 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.en_US
dc.subjectComputer Scienceen_US
dc.titleActivity classification using a single wrist-worn accelerometeren_US
dc.typeConference Proceedingen_US
article.title.sourcetitleSKIMA 2011 - 5th International Conference on Software, Knowledge Information, Industrial Management and Applicationsen_US Mai Universityen_US Universityen_US
Appears in Collections:CMUL: Journal Articles

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