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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UUM Press
2018-02-01
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| Series: | Journal of ICT |
| Online Access: | https://www.scienceopen.com/document?vid=33faf0bd-1c78-444f-a906-ca40bb6f90aa |
| _version_ | 1818858796385042432 |
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| author | Raihani Mohamed Mohammad Noorazlan Shah Zainudin Md. Nasir Sulaiman Thinagaran Perumal Norwati Mustapha |
| author_facet | Raihani Mohamed Mohammad Noorazlan Shah Zainudin Md. Nasir Sulaiman Thinagaran Perumal Norwati Mustapha |
| author_sort | Raihani Mohamed |
| collection | DOAJ |
| 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 | 2024-12-19T09:01:59Z |
| format | Article |
| id | doaj.art-5365880e9c1f4c1ca0fd8d9a211ac550 |
| institution | Directory Open Access Journal |
| issn | 1675-414X |
| language | English |
| last_indexed | 2024-12-19T09:01:59Z |
| publishDate | 2018-02-01 |
| publisher | UUM Press |
| record_format | Article |
| series | Journal of ICT |
| spelling | doaj.art-5365880e9c1f4c1ca0fd8d9a211ac5502022-12-21T20:28:27ZengUUM PressJournal of ICT1675-414X2018-02-0110.32890/jict2018.17.2.8252MULTI-LABEL CLASSIFICATION FOR PHYSICAL ACTIVITY RECOGNITION FROM VARIOUS ACCELEROMETER SENSOR POSITIONSRaihani MohamedMohammad Noorazlan Shah ZainudinMd. Nasir SulaimanThinagaran PerumalNorwati MustaphaIn 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.https://www.scienceopen.com/document?vid=33faf0bd-1c78-444f-a906-ca40bb6f90aa |
| spellingShingle | Raihani Mohamed Mohammad Noorazlan Shah Zainudin Md. Nasir Sulaiman Thinagaran Perumal Norwati Mustapha MULTI-LABEL CLASSIFICATION FOR PHYSICAL ACTIVITY RECOGNITION FROM VARIOUS ACCELEROMETER SENSOR POSITIONS Journal of ICT |
| 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 | https://www.scienceopen.com/document?vid=33faf0bd-1c78-444f-a906-ca40bb6f90aa |
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