Privacy-aware IoT based fall detection with infrared sensors and deep learning

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.