Multi-Occupancy Fall Detection Using Non-Invasive Thermal Vision Sensor

Falling is a common issue within the aging population. The immediate detection of a fall is key to guarantee early and immediate attention to avoid other potential immobility risks and reduction in recovery time. Video-based approaches for monitoring fall detection, although being highly accurate, are largely perceived as being intrusive if deployed within living environments. As an alternative, thermal vision-based methods can be deployed to offer a more acceptable level of privacy. To date, thermal vision-based fall detection methods have largely focused on single-occupancy scenarios, which are not fully representative of real living environments with multi-occupancy. This work proposes a non-invasive thermal vision-based approach of multi-occupancy fall detection (MoT-LoGNN) which discriminates between a fall or no-fall. The approach consists of four major components: i) a multi-occupancy decomposer, ii) a sensitivity-based sample selector, iii) the T-LoGNN for single-occupancy fall detection, and iv) a fine-tuning mechanism. The T-LoGNN consists of a robust neural network minimizing a Localized Generalization Error (L-GEM) and thermal image features extracted by a Convolutional Neural Network (CNN). Comparing to other methods, the MoT-LoGNN achieved the highest average accuracy of 98.39% within the context of a multi-occupancy fall detection experiment.