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.