Literature DB >> 35680453

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

Ekram Alam1, Abu Sufian2, Paramartha Dutta3, Marco Leo4.   

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.
Copyright © 2022 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Accuracy; Fall Detection Metrics; Human Fall Datasets; Human Fall Detection; Le2i Fall Detection Dataset; Multiple Camera Fall Dataset; Sensitivity; Specificity; URFD

Mesh:

Year:  2022        PMID: 35680453     DOI: 10.1016/j.compbiomed.2022.105626

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   6.698


  1 in total

1.  A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition.

Authors:  Zhenxiao Zhao; Lei Zhang; Huiliang Shang
Journal:  Sensors (Basel)       Date:  2022-07-22       Impact factor: 3.847

  1 in total

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