VRFfall: Cross Vision-RF Fall Detection with Camera and mmWave Radar
Accurate fall detection systems are vital to address the global health concern of elderly falls, which often lead to severe injuries, hospitalizations, and fatalities. Since falls can happen at any time in any location, it is imperative to have a comprehensive system that boasts high applicability a...
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| Vydáno v: | Proceedings - International Conference on Parallel and Distributed Systems s. 100 - 107 |
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IEEE
10.10.2024
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| ISSN: | 2690-5965 |
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| Abstract | Accurate fall detection systems are vital to address the global health concern of elderly falls, which often lead to severe injuries, hospitalizations, and fatalities. Since falls can happen at any time in any location, it is imperative to have a comprehensive system that boasts high applicability across a broad range of scenarios, operating seamlessly 24/7. However, within a range of fall detection systems, most of the existing work is built upon mono-modal sensors, which are inevitably inherited and constrained by mono-modal shortages. To overcome the constraints of mono-modal systems, we introduce VRFfall, a novel multi-modal fall detection system that seamlessly fuses mmWave radar and camera technologies. As a system with high generalization capabilities, VRFfall supports both multi-modal and mono-modal inputs with its independent feature extraction pipeline for each modality. Utilizing a cross-modal knowledge transfer design, VRFfall enhances performance with mono-modal input by leveraging fused knowledge from the other modality. Moreover, to ensure optimal fusion decisions under modal discrepancies, VRFfall incorporates an adaptive Modal Quality Assessment Module (MQAM) that dynamically evaluates and fuses features from both modalities. Extensive evaluations using a dataset collected from 20 volunteers across two environments and three conditions have been conducted on VRFfall. The results demonstrate its high performance and excellent generalization across diverse environments and conditions, promising a 24/7 continuous fall detection system. |
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| AbstractList | Accurate fall detection systems are vital to address the global health concern of elderly falls, which often lead to severe injuries, hospitalizations, and fatalities. Since falls can happen at any time in any location, it is imperative to have a comprehensive system that boasts high applicability across a broad range of scenarios, operating seamlessly 24/7. However, within a range of fall detection systems, most of the existing work is built upon mono-modal sensors, which are inevitably inherited and constrained by mono-modal shortages. To overcome the constraints of mono-modal systems, we introduce VRFfall, a novel multi-modal fall detection system that seamlessly fuses mmWave radar and camera technologies. As a system with high generalization capabilities, VRFfall supports both multi-modal and mono-modal inputs with its independent feature extraction pipeline for each modality. Utilizing a cross-modal knowledge transfer design, VRFfall enhances performance with mono-modal input by leveraging fused knowledge from the other modality. Moreover, to ensure optimal fusion decisions under modal discrepancies, VRFfall incorporates an adaptive Modal Quality Assessment Module (MQAM) that dynamically evaluates and fuses features from both modalities. Extensive evaluations using a dataset collected from 20 volunteers across two environments and three conditions have been conducted on VRFfall. The results demonstrate its high performance and excellent generalization across diverse environments and conditions, promising a 24/7 continuous fall detection system. |
| Author | Zhu, Yanying Sun, Min Song, Haotian Zhou, Li Wu, Kaishun |
| Author_xml | – sequence: 1 givenname: Yanying surname: Zhu fullname: Zhu, Yanying email: yzhu367@connect.hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology(Guangzhou) Guangzhou,Thrust of Data Science and Analytics,China – sequence: 2 givenname: Haotian surname: Song fullname: Song, Haotian email: haotiansong@hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology(Guangzhou),Thrust of Internet of Things,Guangzhou,China – sequence: 3 givenname: Kaishun surname: Wu fullname: Wu, Kaishun email: wuks@hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology(Guangzhou) Guangzhou,Thrust of Data Science and Analytics,China – sequence: 4 givenname: Min surname: Sun fullname: Sun, Min email: sunmin@chinamobile.com organization: China Mobile Information Technology,Shenzhen,China – sequence: 5 givenname: Li surname: Zhou fullname: Zhou, Li email: zhouli@chinamobile.com organization: China Mobile Information Technology,Shenzhen,China |
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| Snippet | Accurate fall detection systems are vital to address the global health concern of elderly falls, which often lead to severe injuries, hospitalizations, and... |
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| SubjectTerms | Cameras Fall detection Fuses Human Sensing Knowledge transfer Millimeter wave communication Multi-modal Quality assessment Radar Radar detection Sensor systems Sensors Wireless Sensing |
| Title | VRFfall: Cross Vision-RF Fall Detection with Camera and mmWave Radar |
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