mlsanityData to 7 Oct 2026

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

arXiv 2207.10952 ↗First posted 22 Jul 2022cs.CV cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 157 references that are in the map. First posted on arXiv 22 Jul 2022.

Builds on

31 of 157 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Reviewcited by 6
  2. 2024Real-Time Human Fall Detection using a Lightweight Pose Estimation Techniquecited by 0
  3. 2024Cutup and Detect: Human Fall Detection on Cutup Untrimmed Videos Using a Large Foundational Video Understanding Modelcited by 0
  4. 2025Human Fall Detection using Transfer Learning-based 3D CNNcited by 0
  5. 2025Thermal Imaging-based Real-time Fall Detection using Motion Flow and Attention-enhanced Convolutional Recurrent Architecturecited by 0
  6. 2025MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillationcited by 0
  7. 2026Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysiscited by 0