Abstract
Unattended falls, where the injured person may be unconscious, are among the most frequent causes of death worldwide among the elderly population. Several fall detection methods using devices, such as wearable sensors and cameras, have been deployed in Smart Homes to detect and minimize the response time against these types of accidents. Some related works have proposed to trigger an alarm when a fall is detected. However, there is a lack of considering the state in which the person has been left after the fall. This work proposes a fall detection system using low-resolution thermal cameras that detects falls and classifies the falls depending on the state of conscience of the user. Thus, once detected in a risky state, a voice assistant routine starts to measure the degree of conscience of the person and make an emergency call only when necessary. The system is deployed in the Smart Home of the University of Almeria and validated with one user, triggering the alarm in all falls that lead to an unconscious state.
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Acknowledgements
This research was funded by the Spanish Ministry of Economy and Competitiveness grant number RTI2018-095993-B-I00; by the Junta de Andalucía grant number P18-RT-1193 and by the University of Almería grant number UAL18-TIC-A020-B. Marcos Lupión Lorente is a fellow of the Spanish ‘Formación del Profesorado Universitario’ program (FPU19/02756).
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Lupión, M., González-Ruiz, V., Sanjuan, J.F., Medina-Quero, J., Ortigosa, P.M. (2022). Detection of Unconsciousness in Falls Using Thermal Vision Sensors. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_1
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