# Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN

arXiv: https://arxiv.org/abs/2006.06201 · HTML: https://mlsanity.com/paper/2006.06201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://mlsanity.com/paper/2207.10952/) (2022)
- [SDFA: Structure Aware Discriminative Feature Aggregation for Efficient Human Fall Detection in Video](https://mlsanity.com/paper/2503.07008/) (2025)
- [Modeling Human Skeleton Joint Dynamics for Fall Detection](https://mlsanity.com/paper/2503.06938/) (2025)

## Abstract (arXiv)

Fall detection in specialized homes for the elderly is challenging. Vision-based fall detection solutions have a significant advantage over sensor-based ones as they do not instrument the resident who can suffer from mental diseases. This work is part of a project intended to deploy fall detection solutions in nursing homes. The proposed solution, based on Deep Learning, is built on a Convolutional Neural Network (CNN) trained to maximize a sensitivity-based metric. This work presents the requirements from the medical side and how it impacts the tuning of a CNN. Results highlight the importance of the temporal aspect of a fall. Therefore, a custom metric adapted to this use case and an implementation of a decision-making process are proposed in order to best meet the medical teams requirements. Clinical relevance This work presents a fall detection solution enabled to detect 86.2% of falls while producing only 11.6% of false alarms in average on the considered databases.