Vision-based Human Fall Detection Systems using Deep Learning: A Review

TL;DR

This review paper discusses recent deep learning-based vision systems for human fall detection, highlighting datasets, evaluation metrics, and future research directions to improve assistive living for vulnerable populations.

Contribution

It provides a comprehensive survey of current deep learning methods and benchmark datasets for vision-based fall detection, and discusses evaluation metrics and future prospects.

Findings

Deep learning techniques are effective for non-intrusive fall detection.

Several benchmark datasets are available for evaluating fall detection systems.

Future research should focus on improving accuracy and real-time performance.

Abstract

Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.