Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs
Although human activity recognition (HAR) has been studied extensively in the past decade, HAR on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/503291 |
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| _version_ | 1849696923933474816 |
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| author | Adil Mehmood Khan Ali Tufail Asad Masood Khattak Teemu H. Laine |
| author_facet | Adil Mehmood Khan Ali Tufail Asad Masood Khattak Teemu H. Laine |
| author_sort | Adil Mehmood Khan |
| collection | DOAJ |
| description | Although human activity recognition (HAR) has been studied extensively in the past decade, HAR on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing this goal is challenging, however. Firstly, these devices are low on resources, which limits the number of sensors that can be utilized. Secondly, to achieve optimum performance efficient feature extraction, feature selection and classification methods are required. This work implements a smartphone-based HAR scheme in accordance with these requirements. Time domain features are extracted from only three smartphone sensors, and a nonlinear discriminatory approach is employed to recognize 15 activities with a high accuracy. This approach not only selects the most relevant features from each sensor for each activity but it also takes into account the differences resulting from carrying a phone at different positions. Evaluations are performed in both offline and online settings. Our comparison results show that the proposed system outperforms some previous mobile phone-based HAR systems. |
| format | Article |
| id | doaj-art-5238e2c64c364804b9a616bf7f00215b |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5238e2c64c364804b9a616bf7f00215b2025-08-20T03:19:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-05-011010.1155/2014/503291503291Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMsAdil Mehmood Khan0Ali Tufail1Asad Masood Khattak2Teemu H. Laine3 School of Information and Computer Engineering, Ajou University, San 5, Woncheon-dong, Suwon-si, Gyeonggi-do 443-749, Republic of Korea Department of Computer Engineering, Yildirim Beyazit University, Altindag Ulus, 06030 Ankara, Turkey Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea School of Information and Computer Engineering, Ajou University, San 5, Woncheon-dong, Suwon-si, Gyeonggi-do 443-749, Republic of KoreaAlthough human activity recognition (HAR) has been studied extensively in the past decade, HAR on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing this goal is challenging, however. Firstly, these devices are low on resources, which limits the number of sensors that can be utilized. Secondly, to achieve optimum performance efficient feature extraction, feature selection and classification methods are required. This work implements a smartphone-based HAR scheme in accordance with these requirements. Time domain features are extracted from only three smartphone sensors, and a nonlinear discriminatory approach is employed to recognize 15 activities with a high accuracy. This approach not only selects the most relevant features from each sensor for each activity but it also takes into account the differences resulting from carrying a phone at different positions. Evaluations are performed in both offline and online settings. Our comparison results show that the proposed system outperforms some previous mobile phone-based HAR systems.https://doi.org/10.1155/2014/503291 |
| spellingShingle | Adil Mehmood Khan Ali Tufail Asad Masood Khattak Teemu H. Laine Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs International Journal of Distributed Sensor Networks |
| title | Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs |
| title_full | Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs |
| title_fullStr | Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs |
| title_full_unstemmed | Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs |
| title_short | Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs |
| title_sort | activity recognition on smartphones via sensor fusion and kda based svms |
| url | https://doi.org/10.1155/2014/503291 |
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