Grassmann manifold based framework for automated fall detection from a camera

Abstract

In this paper, we propose a Grassmann manifold based novel, real-time framework for automated fall detection in an indoor environment using a single camera. Fall is an activity that is uncontrolled, unintentional, involuntary and can occur while a person is doing any daily living activity. This is especially true for the elderly and the sick, for whom a fall can lead to further complications that may cause irreversible damage to their health. Therefore, it is important to develop a non-intrusive, automated fall detection method such that an alert can be raised in case a fall occurs. We propose a Grassmann manifold based framework for fall detection from a single camera that is also capable of recognizing other daily living activities (DLA) such as walking, sitting, etc. We perform experiments using publicly available datasets and our experimental results show that the fall detection and recognition accuracy of our proposed framework is comparable with the state of the art.