Abstract

Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons.

Highlights

  • We present a method to classify between fall and non-fall events, which is based on computing the similarity between video sequences

  • We found that V2V-PoseNet is the most accurate to detect skeletons from videos

  • We evaluated the performance of our approach on the Charfi [52] and UR Fall Detection [53] datasets, and compared the results against state-of-the-art methods as reported below

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In 2019, the United Nations (UN) published statistics about the world population [1]. In the years, the percentage of elderly people will grow considerably in Sub-Saharan Africa, Northern Africa, Western Asia, Latina America, Caribbean, Australia, North America, etc.In the same document, the estimated change in the percentage of elderly people between 2019 and 2050 is reported. The number of persons over 65 years in Morocco is expected to increase from 7.3% of population in 2019 to 11.2% of population in 2030

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