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Activity classification using a single wrist-worn accelerometer

Chernbumroong, S.; Atkins, A. S.; Yu, Hongnians

Authors

S. Chernbumroong

A. S. Atkins



Abstract

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.

Presentation Conference Type Conference Paper (Published)
Conference Name 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA 2011)
Start Date Sep 8, 2011
End Date Sep 11, 2011
Online Publication Date Dec 1, 2011
Publication Date 2011-09
Deposit Date Jun 15, 2022
Publisher Institute of Electrical and Electronics Engineers
Book Title 2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings
DOI https://doi.org/10.1109/skima.2011.6089975
Public URL http://researchrepository.napier.ac.uk/Output/2879259