Vision-Based Fallen Person Detection for the Elderly

TL;DR

This paper introduces a non-invasive, stereo camera-based system utilizing CNN human pose estimation to detect falls among the elderly, aiming to improve response times and reduce injuries.

Contribution

It presents a novel fall detection method combining stereo vision and CNN-based pose estimation, with a reasoning module for accurate fall identification.

Findings

High accuracy in diverse scenarios

Almost no misclassification

Publicly available implementation

Abstract

Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper we present a new , non-invasive system for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. Furthermore, our system consists of a reasoning module which formulates a number of measures to reason whether a person is fallen. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification. To reproduce our results, the implementation is publicly available to the scientific community.