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

arXiv: https://arxiv.org/abs/2207.10952 · HTML: https://mlsanity.com/paper/2207.10952/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 157 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation](https://mlsanity.com/paper/1511.06645/) (2015)
- [A Twofold Siamese Network for Real-Time Object Tracking](https://mlsanity.com/paper/1802.08817/) (2018)
- [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](https://mlsanity.com/paper/1505.04868/) (2015)
- [MatConvNet - Convolutional Neural Networks for MATLAB](https://mlsanity.com/paper/1412.4564/) (2014)
- [Advancements in Image Classification using Convolutional Neural Network](https://mlsanity.com/paper/1905.03288/) (2019)
- [Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM](https://mlsanity.com/paper/2007.07773/) (2020)
- [Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging](https://mlsanity.com/paper/2004.08352/) (2020)
- [Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow](https://mlsanity.com/paper/2104.05744/) (2021)
- [SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience](https://mlsanity.com/paper/2004.00797/) (2020)
- [Vision-Based Fallen Person Detection for the Elderly](https://mlsanity.com/paper/1707.07608/) (2017)
- [Video Based Fall Detection Using Human Poses](https://mlsanity.com/paper/2107.14633/) (2021)
- [A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring](https://mlsanity.com/paper/2105.02960/) (2021)
- [Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN](https://mlsanity.com/paper/2006.06201/) (2020)

## Built on by (most cited first, 7 of 7)

- [Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review](https://mlsanity.com/paper/2401.11790/) (2024)
- [Real-Time Human Fall Detection using a Lightweight Pose Estimation Technique](https://mlsanity.com/paper/2401.01587/) (2024)
- [Cutup and Detect: Human Fall Detection on Cutup Untrimmed Videos Using a Large Foundational Video Understanding Model](https://mlsanity.com/paper/2401.16280/) (2024)
- [Human Fall Detection using Transfer Learning-based 3D CNN](https://mlsanity.com/paper/2506.03193/) (2025)
- [Thermal Imaging-based Real-time Fall Detection using Motion Flow and Attention-enhanced Convolutional Recurrent Architecture](https://mlsanity.com/paper/2509.16479/) (2025)
- [MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillation](https://mlsanity.com/paper/2510.03601/) (2025)
- [Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis](https://mlsanity.com/paper/2606.23292/) (2026)

## 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.