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- Title
Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework.
- Authors
Zheng, Yuhuang
- Abstract
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) algorithm to 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.
- Subjects
HUMAN activity recognition; FEATURE selection; ACCELEROMETERS; FUNCTIONAL assessment; NAIVE Bayes classification; LEAST squares; SUPPORT vector machines
- Publication
Journal of Electrical & Computer Engineering, 2015, Vol 2015, p1
- ISSN
2090-0147
- Publication type
Article
- DOI
10.1155/2015/140820