Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home
Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity reco...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720971513 |
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| _version_ | 1849704997452775424 |
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| author | Yan Hu Bingce Wang Yuyan Sun Jing An Zhiliang Wang |
| author_facet | Yan Hu Bingce Wang Yuyan Sun Jing An Zhiliang Wang |
| author_sort | Yan Hu |
| collection | DOAJ |
| description | Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach. |
| format | Article |
| id | doaj-art-011a0478b91a4758a08c07fc81823cd4 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-011a0478b91a4758a08c07fc81823cd42025-08-20T03:16:35ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-11-011610.1177/1550147720971513Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart homeYan Hu0Bingce Wang1Yuyan Sun2Jing An3Zhiliang Wang4Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Cyber Security, University of Chinese Academy of Sciences, Beijing, ChinaSchool of Marxism Studies, University of Science and Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaHealth smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.https://doi.org/10.1177/1550147720971513 |
| spellingShingle | Yan Hu Bingce Wang Yuyan Sun Jing An Zhiliang Wang Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home International Journal of Distributed Sensor Networks |
| title | Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home |
| title_full | Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home |
| title_fullStr | Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home |
| title_full_unstemmed | Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home |
| title_short | Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home |
| title_sort | genetic algorithm optimized support vector machine for real time activity recognition in health smart home |
| url | https://doi.org/10.1177/1550147720971513 |
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