Human Fall Detection from Sequences of Skeleton Features using Vision Transformer

Abstract

Detecting human falls is an exciting topic that can be approached in a number of ways. In recent years, several approaches have been suggested. These methods aim at determining whether a person is walking normally, standing, or falling, among other activities. The detection of falls in the elderly population is essential for preventing major medical consequences and early intervention mitigates the effects of such accidents. However, the medical team must be very vigilant, monitoring people constantly, something that is time consuming, expensive, intrusive and not always accurate. In this paper, we propose an approach to automatically identify human fall activity using visual data to timely warn the appropriate caregivers and authorities. The proposed approach detects human falls using a vision transformer. A Multi-headed transformer encoder model learns typical human behaviour based on skeletonized human data. The proposed method has been evaluated on the UR-Fall and UP-Fall datasets, with an accuracy of 96.12%, 97.36% respectively using RP normalization and linear interpolation comparable to state-of-the-art methods.