Fall detection without people: A simulation approach tackling video data scarcity

We propose an intelligent system to detect human fall events using a physics-based myoskeletal simulation, detecting falls by comparing the simulation with a fall velocity profile using the Hausdorff distance. Previous methods of fall detection are trained using recordings of acted falls which are limited in number, body variability and type of fall and can be unrepresentative of real falls. The paper demonstrates that the use of fall recordings are unnecessary for modelling the fall as the simulation engine can produce a variety of fall events customised to an individual's physical characteristics using myoskeletal models of different morphology, without pre-knowledge of the falling behaviour. To validate this methodological approach, the simulation is customised by the person's height, modelling a rigid fall type. This approach allows the detection to be tailored to cohorts in the population (such as the elderly or the infirm) that are not represented in existing fall datasets. The method has been evaluated on several publicly available datasets which show that our method outperforms the results of previously reported research in fall detection. Finally, our approach is demonstrated to be robust to occlusions that hide up to 50% of a fall, which increases the applicability of automatic fall detection in a real-world environment such as the home.