Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework
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 recognit...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/140820 |
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| _version_ | 1832547678656921600 |
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| author | Yuhuang Zheng |
| author_facet | Yuhuang Zheng |
| author_sort | Yuhuang Zheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f75a120d14284f3fa67c970c0c4c63f4 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-f75a120d14284f3fa67c970c0c4c63f42025-02-03T06:43:53ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/140820140820Human Activity Recognition Based on the Hierarchical Feature Selection and Classification FrameworkYuhuang Zheng0Department of Physics, Guangdong University of Education, Guangzhou 510303, ChinaHuman 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.http://dx.doi.org/10.1155/2015/140820 |
| spellingShingle | Yuhuang Zheng Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework Journal of Electrical and Computer Engineering |
| title | Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework |
| title_full | Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework |
| title_fullStr | Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework |
| title_full_unstemmed | Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework |
| title_short | Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework |
| title_sort | human activity recognition based on the hierarchical feature selection and classification framework |
| url | http://dx.doi.org/10.1155/2015/140820 |
| work_keys_str_mv | AT yuhuangzheng humanactivityrecognitionbasedonthehierarchicalfeatureselectionandclassificationframework |