Multi-label classification for physical activity recognition from various accelerometer sensor positions
In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to be explored and needs to be further investigated. In this study, we investigated the role of sensor place...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59715/ http://psasir.upm.edu.my/id/eprint/59715/1/Multi-label%20classification%20for%20physical%20activity%20recognition%20from%20various%20accelerometer%20sensor%20positions.pdf |
| _version_ | 1848853998513160192 |
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| author | Mohamed, Raihani Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Perumal, Thinagaran Mustapha, Norwati |
| author_facet | Mohamed, Raihani Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Perumal, Thinagaran Mustapha, Norwati |
| author_sort | Mohamed, Raihani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to be explored and needs to be further investigated. In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities. Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately. Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared with several state-of-the-art traditional multi-class classification approaches. The multi-label classification result significantly outperformed the traditional multi-class classification methods as well as minimized the model build time. |
| first_indexed | 2025-11-15T11:02:53Z |
| format | Article |
| id | upm-59715 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:02:53Z |
| publishDate | 2018 |
| publisher | Universiti Utara Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-597152018-03-20T03:38:58Z http://psasir.upm.edu.my/id/eprint/59715/ Multi-label classification for physical activity recognition from various accelerometer sensor positions Mohamed, Raihani Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Perumal, Thinagaran Mustapha, Norwati In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to be explored and needs to be further investigated. In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities. Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately. Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared with several state-of-the-art traditional multi-class classification approaches. The multi-label classification result significantly outperformed the traditional multi-class classification methods as well as minimized the model build time. Universiti Utara Malaysia Press 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59715/1/Multi-label%20classification%20for%20physical%20activity%20recognition%20from%20various%20accelerometer%20sensor%20positions.pdf Mohamed, Raihani and Zainudin, Muhammad Noorazlan Shah and Sulaiman, Md. Nasir and Perumal, Thinagaran and Mustapha, Norwati (2018) Multi-label classification for physical activity recognition from various accelerometer sensor positions. Journal of Information and Communication Technology, 18 (2). pp. 209-231. ISSN 1675-414X; ESSN: 2180-3862 http://jict.uum.edu.my/index.php/current-issues |
| spellingShingle | Mohamed, Raihani Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Perumal, Thinagaran Mustapha, Norwati Multi-label classification for physical activity recognition from various accelerometer sensor positions |
| title | Multi-label classification for physical activity recognition from various accelerometer sensor positions |
| title_full | Multi-label classification for physical activity recognition from various accelerometer sensor positions |
| title_fullStr | Multi-label classification for physical activity recognition from various accelerometer sensor positions |
| title_full_unstemmed | Multi-label classification for physical activity recognition from various accelerometer sensor positions |
| title_short | Multi-label classification for physical activity recognition from various accelerometer sensor positions |
| title_sort | multi-label classification for physical activity recognition from various accelerometer sensor positions |
| url | http://psasir.upm.edu.my/id/eprint/59715/ http://psasir.upm.edu.my/id/eprint/59715/ http://psasir.upm.edu.my/id/eprint/59715/1/Multi-label%20classification%20for%20physical%20activity%20recognition%20from%20various%20accelerometer%20sensor%20positions.pdf |