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基于视觉的深度学习人体跌倒检测系统:综述。

Vision-based human fall detection systems using deep learning: A review.

机构信息

Department of Computer Science, Gour Mahavidyalaya, West Bengal, India.

Department of Computer Science, University of Gour Banga, India.

出版信息

Comput Biol Med. 2022 Jul;146:105626. doi: 10.1016/j.compbiomed.2022.105626. Epub 2022 May 27.

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-arts 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.

摘要

人摔倒(fall)是一个非常严重的健康问题,尤其是对独居的老人和残疾人而言。全球范围内老年人口的数量正在稳步增加。因此,人摔倒检测(detection)成为了为这些人提供辅助生活的有效技术。在辅助生活中,深度学习和计算机视觉已经得到了广泛应用。在这篇综述文章中,我们讨论了基于深度学习(DL)的非侵入式(基于视觉)摔倒检测技术的最新进展。我们还对摔倒检测基准数据集进行了调查。为了清晰起见,我们简要讨论了用于评估摔倒检测系统性能的不同指标。本文还对基于视觉的人体摔倒检测技术给出了未来的发展方向。

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