Journal
JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING
Volume 2015, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2015/140820
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Funding
- Guangdong University of Education [11ARF04]
- Guangdong Provincial Department of Education [2013LYM 0063, 2014GXJK161]
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Human activity recognition via triaxial accelerometers can provide valuable information for evaluating functional abilities. In this paper, we present an accelerometer sensor-based approach for human activity recognition. Our proposed recognition method used a hierarchical scheme, where the recognition of ten activity classes was divided into five distinct classification problems. Every classifier used the Least Squares Support Vector Machine (LS-SVM) and Naive Bayes (NB) algorithmto distinguish different activity classes. The activity class was recognized based on the mean, variance, entropy of magnitude, and angle of triaxial accelerometer signal features. Our proposed activity recognition method recognized ten activities with an average accuracy of 95.6% using only a single triaxial accelerometer.
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