Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion
With the rapid growth of the elderly population, smart elderly care has become a crucial solution to address this societal challenge, with safety concerns being paramount. Existing research often focuses on fall detection and localization, but overlooks the comprehensive identification of hazards in...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Array |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259000562500030X |
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| author | Peng Gao Naji Alhusaini Jinjun Liu Liang Zhao Yiwen Zhang |
| author_facet | Peng Gao Naji Alhusaini Jinjun Liu Liang Zhao Yiwen Zhang |
| author_sort | Peng Gao |
| collection | DOAJ |
| description | With the rapid growth of the elderly population, smart elderly care has become a crucial solution to address this societal challenge, with safety concerns being paramount. Existing research often focuses on fall detection and localization, but overlooks the comprehensive identification of hazards in home environments. This work proposes a preventive hazard detection and safety assessment paradigm based on infrared visible dual-mode fusion, aiming to identify various potential hazards, including the risk of falling, and achieve a paradigm shift from “post response” to “pre prevention”. The model is designed with a channel space dual attention Transformer and a multi-scale adaptive fusion module, which improves the accuracy of hazard detection under different lighting conditions. Experimental results show a 15% improvement in detection accuracy over single-modality images on a custom-collected dataset. Compared to state-of-the-art methods such as DATFuse, IPLF, and Res2Fusion, our approach improves the mean Average Precision by 10%, with higher precision and recall in complex environments. Additionally, a Bayesian-optimized lightweight CNN achieves a 30% reduction in model size while maintaining high accuracy, making it suitable for deployment on resource-constrained devices. This study provides a robust tool for enhancing elderly safety in home environments and establishes a solid foundation for future research in smart elderly care. |
| format | Article |
| id | doaj-art-52c95d0999fd4b2dbabbe9f534c31f21 |
| institution | DOAJ |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj-art-52c95d0999fd4b2dbabbe9f534c31f212025-08-20T03:21:38ZengElsevierArray2590-00562025-07-012610040310.1016/j.array.2025.100403Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusionPeng Gao0Naji Alhusaini1Jinjun Liu2Liang Zhao3Yiwen Zhang4School of Artificial Intelligence, Chuzhou University, Chuzhou, China; School of Computer Science and Technology, Anhui University, Hefei, ChinaSchool of Artificial Intelligence, Chuzhou University, Chuzhou, China; Corresponding author.School of Artificial Intelligence, Chuzhou University, Chuzhou, ChinaSchool of Artificial Intelligence, Chuzhou University, Chuzhou, ChinaSchool of Computer Science and Technology, Anhui University, Hefei, ChinaWith the rapid growth of the elderly population, smart elderly care has become a crucial solution to address this societal challenge, with safety concerns being paramount. Existing research often focuses on fall detection and localization, but overlooks the comprehensive identification of hazards in home environments. This work proposes a preventive hazard detection and safety assessment paradigm based on infrared visible dual-mode fusion, aiming to identify various potential hazards, including the risk of falling, and achieve a paradigm shift from “post response” to “pre prevention”. The model is designed with a channel space dual attention Transformer and a multi-scale adaptive fusion module, which improves the accuracy of hazard detection under different lighting conditions. Experimental results show a 15% improvement in detection accuracy over single-modality images on a custom-collected dataset. Compared to state-of-the-art methods such as DATFuse, IPLF, and Res2Fusion, our approach improves the mean Average Precision by 10%, with higher precision and recall in complex environments. Additionally, a Bayesian-optimized lightweight CNN achieves a 30% reduction in model size while maintaining high accuracy, making it suitable for deployment on resource-constrained devices. This study provides a robust tool for enhancing elderly safety in home environments and establishes a solid foundation for future research in smart elderly care.http://www.sciencedirect.com/science/article/pii/S259000562500030XSmart elderly careHazard detectionRisk assessmentMulti-scale adaptive fusionInfrared images |
| spellingShingle | Peng Gao Naji Alhusaini Jinjun Liu Liang Zhao Yiwen Zhang Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion Array Smart elderly care Hazard detection Risk assessment Multi-scale adaptive fusion Infrared images |
| title | Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion |
| title_full | Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion |
| title_fullStr | Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion |
| title_full_unstemmed | Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion |
| title_short | Risk assessment and physical hazard detection in elderly living environments using multi-scale infrared and visible imagery fusion |
| title_sort | risk assessment and physical hazard detection in elderly living environments using multi scale infrared and visible imagery fusion |
| topic | Smart elderly care Hazard detection Risk assessment Multi-scale adaptive fusion Infrared images |
| url | http://www.sciencedirect.com/science/article/pii/S259000562500030X |
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