Hybrid deep learning in remote passive fall detection

Xiaofeng An; Rodolfo C. Raga Jr.

doi:10.1117/12.2685544

28 July 2023 Hybrid deep learning in remote passive fall detection

Xiaofeng An, Rodolfo C. Raga Jr.

Proceedings Volume 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023); 127160D (2023) https://doi.org/10.1117/12.2685544
Event: Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 2023, Xi'an, China

Abstract

The camera image recognition and active wearable sensor scheme are classic methods in fall detection, but the camera scheme has the problem of privacy leakage, while the active wearable sensor scheme has the problem of wearable comfort. This paper proposes a method of human motion recognition using passive sensors and fall detection using hybrid depth learning model, and designs an intelligent human motion sensing clothing based on passive RFID tag positioning technology. The experimental results show that using passive RFID tag as a sensor scheme can achieve efficient and accurate fall detection, and achieve a recognition rate of 99.5%.The proposed method does not require additional equipment, realizes non-invasive monitoring, and is comfortable to wear and inexpensive. It can be widely used in other fields such as medical health, family safety, old-age care and so on. It is of great significance to improve the practicability of fall detection.

Citation Download Citation

Xiaofeng An and Rodolfo C. Raga Jr. "Hybrid deep learning in remote passive fall detection", Proc. SPIE 12716, Third International Conference on Digital Signal and Computer Communications (DSCC 2023), 127160D (28 July 2023); https://doi.org/10.1117/12.2685544

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