A Multimodal Dangerous State Recognition and Early Warning System for Elderly with Intermittent Dementia
<|reference_start|>A Multimodal Dangerous State Recognition and Early Warning System for Elderly with Intermittent Dementia: In response to the social issue of the increasing number of elderly vulnerable groups going missing due to the aggravating aging population in China, our team has developed a wearable anti-loss device and intelligent early warning system for elderly individuals with intermittent dementia using artificial intelligence and IoT technology. This system comprises an anti-loss smart helmet, a cloud computing module, and an intelligent early warning application on the caregiver's mobile device. The smart helmet integrates a miniature camera module, a GPS module, and a 5G communication module to collect first-person images and location information of the elderly. Data is transmitted remotely via 5G, FTP, and TCP protocols. In the cloud computing module, our team has proposed for the first time a multimodal dangerous state recognition network based on scene and location information to accurately assess the risk of elderly individuals going missing. Finally, the application software interface designed for the caregiver's mobile device implements multi-level early warnings. The system developed by our team requires no operation or response from the elderly, achieving fully automatic environmental perception, risk assessment, and proactive alarming. This overcomes the limitations of traditional monitoring devices, which require active operation and response, thus avoiding the issue of the digital divide for the elderly. It effectively prevents accidental loss and potential dangers for elderly individuals with dementia.<|reference_end|>
@article{deng2024a,
    title={A Multimodal Dangerous State Recognition and Early Warning System for
  Elderly with Intermittent Dementia},
    author={Liyun Deng, Lei Jin, Guangcheng Wang, Quan Shi, Han Wang},
    journal={arXiv preprint arXiv:2405.20136},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20136},
    primaryClass={cs.CV}
}