# SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience

arXiv: https://arxiv.org/abs/2004.00797 · HTML: https://mlsanity.com/paper/2004.00797/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://mlsanity.com/paper/2207.10952/) (2022)
- [Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment](https://mlsanity.com/paper/2408.08182/) (2024)
- [Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos](https://mlsanity.com/paper/2104.04650/) (2021)
- [Video-Based Inpatient Fall Risk Assessment: A Case Study](https://mlsanity.com/paper/2106.07565/) (2021)
- [Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Body](https://mlsanity.com/paper/2208.07291/) (2022)
- [Real-Time Human Fall Detection using a Lightweight Pose Estimation Technique](https://mlsanity.com/paper/2401.01587/) (2024)

## Abstract (arXiv)

Falling can have fatal consequences for elderly people especially if the fallen person is unable to call for help due to loss of consciousness or any injury. Automatic fall detection systems can assist through prompt fall alarms and by minimizing the fear of falling when living independently at home. Existing vision-based fall detection systems lack generalization to unseen environments due to challenges such as variations in physical appearances, different camera viewpoints, occlusions, and background clutter. In this paper, we explore ways to overcome the above challenges and present Single Shot Human Fall Detector (SSHFD), a deep learning based framework for automatic fall detection from a single image. This is achieved through two key innovations. First, we present a human pose based fall representation which is invariant to appearance characteristics. Second, we present neural network models for 3d pose estimation and fall recognition which are resilient to missing joints due to occluded body parts. Experiments on public fall datasets show that our framework successfully transfers knowledge of 3d pose estimation and fall recognition learnt purely from synthetic data to unseen real-world data, showcasing its generalization capability for accurate fall detection in real-world scenarios.