DocumentCode :
2463570
Title :
Activity classification using a single wrist-worn accelerometer
Author :
Chernbumroong, Saisakul ; Atkins, Anthony S. ; Yu, Hongnian
Author_Institution :
Coll. of Arts, Media & Technol., Chiang Mai Univ., Chiang Mai, Thailand
fYear :
2011
fDate :
8-11 Sept. 2011
Firstpage :
1
Lastpage :
6
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.
Keywords :
accelerometers; decision trees; neural nets; signal classification; artificial neural network; automatic activity classification; automatic activity recognition; decision tree C4.5; human activity automatic identification; sensor technology; single wrist-worn accelerometer; Accelerometers; Accuracy; Artificial neural networks; Biological neural networks; Decision trees; Watches; Wrist; Accelerometer; Activity classification; Sensor data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software, Knowledge Information, Industrial Management and Applications (SKIMA), 2011 5th International Conference on
Conference_Location :
Benevento
Print_ISBN :
978-1-4673-0247-0
Type :
conf
DOI :
10.1109/SKIMA.2011.6089975
Filename :
6089975
Link To Document :
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