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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2402.09452 |
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| _version_ | 1866929244118974464 |
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| author | Monjur, Mahathir Liu, Jia Xu, Jingye Zhang, Yuntong Wang, Xiaomeng Li, Chengdong Park, Hyejin Wang, Wei Shieh, Karl Munir, Sirajum Wang, Jing Song, Lixin Nirjon, Shahriar |
| author_facet | Monjur, Mahathir Liu, Jia Xu, Jingye Zhang, Yuntong Wang, Xiaomeng Li, Chengdong Park, Hyejin Wang, Wei Shieh, Karl Munir, Sirajum Wang, Jing Song, Lixin Nirjon, Shahriar |
| contents | This paper examines the application of WiFi signals for real-world monitoring of daily activities in home healthcare scenarios. While the state-of-the-art of WiFi-based activity recognition is promising in lab environments, challenges arise in real-world settings due to environmental, subject, and system configuration variables, affecting accuracy and adaptability. The research involved deploying systems in various settings and analyzing data shifts. It aims to guide realistic development of robust, context-aware WiFi sensing systems for elderly care. The findings suggest a shift in WiFi-based activity sensing, bridging the gap between academic research and practical applications, enhancing life quality through technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09452 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare Monjur, Mahathir Liu, Jia Xu, Jingye Zhang, Yuntong Wang, Xiaomeng Li, Chengdong Park, Hyejin Wang, Wei Shieh, Karl Munir, Sirajum Wang, Jing Song, Lixin Nirjon, Shahriar Signal Processing Machine Learning Systems and Control This paper examines the application of WiFi signals for real-world monitoring of daily activities in home healthcare scenarios. While the state-of-the-art of WiFi-based activity recognition is promising in lab environments, challenges arise in real-world settings due to environmental, subject, and system configuration variables, affecting accuracy and adaptability. The research involved deploying systems in various settings and analyzing data shifts. It aims to guide realistic development of robust, context-aware WiFi sensing systems for elderly care. The findings suggest a shift in WiFi-based activity sensing, bridging the gap between academic research and practical applications, enhancing life quality through technology. |
| title | Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare |
| topic | Signal Processing Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2402.09452 |