Abstract
Falls among the elderly are a major worry for both the elderly and their care-takers, as falls frequently result in severe physical injury. Detecting falls using Internet of Things (IoT) devices can give elderly persons and their care-takers peace of mind in case of emergency. However, due to usability and intrusive nature of wearable and vision-based fall detection has limited acceptability and applicability in washroom and privacy sensitive locations as well as older adults with mental health condition. Privacy-aware infrared array sensors have great potential to identify fall in a non-intrusive way preserving privacy of the subject. Using a secondary dataset, we have utilised and tuned time series based deep learning network to identify fall. Experiments indicate that the time-series based deep learning network offers accuracy of 96.4% using 6 infrared sensors. This result provides encouraging evidence that low-cost privacy-aware infrared array sensor-based fall monitoring can enhance safety and well-being of older adults in self-care or aged care environment.
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Ahamed, F., Shahrestani, S., Cheung, H. (2023). Privacy-Aware IoT Based Fall Detection with Infrared Sensors and Deep Learning. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_33
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