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Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework
Author
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-07
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Zheng, Yuhuang. 2015. Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework. Journal of Electrical and Computer Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1068073
Modern Language Association (MLA)
Zheng, Yuhuang. Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework. Journal of Electrical and Computer Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1068073
American Medical Association (AMA)
Zheng, Yuhuang. Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework. Journal of Electrical and Computer Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1068073
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1068073