A Monocular View-Invariant Fall Detection System for the Elderly in Assisted Home Environments

Abstract

There is an increasing interest in real-time fall detection systems for the elderly in developed countries because more and more elderly are staying alone. There is a great demand for such fall detections systems in the smart home industry and the healthcare industry. Various fall detection approaches have been proposed recently by researchers. However, the majority of the proposed approaches require sensors to be attached on the subjects under surveillance. Sensors are intrusive and restrictive. Moreover, critical situations can often go undetected if the elderly forget to wear those vital sensors. As a result, researchers have recently gained interest in computer vision based solutions. Viewpoint invariance is a very important issue in computer vision because camera position is arbitrary and the subjects are free to move around in the environment. This paper presents a vision-based framework that can detect falls using a single camera, irrespective of the viewpoint of the camera with respect to the subjects. The proposed system makes use of invariant pose models which perform view-invariant human pose recognition. An ensemble of pose models performs inference on each video frame. Each pose model employs an expectation-maximization algorithm to estimate the probability that the given frame contains the corresponding pose. Over a sequence of frames, all the pose models collectively produce a multivariate time series. The system detects falls by analyzing the time series. We utilize the fuzzy hidden Markov model (FHMM) to detect falls. We have performed some experiments on two datasets and the results are found to be promising.