# Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN _by Peyramaure Paul (IETR-Vaader) - 2020.06.04_ ###### tags: `VAADER` `Seminar` ![](https://i.imgur.com/lrp4FKE.png) ## Abstract 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 the SilverConnect project intended to deploy fall detection solutions in nursing homes. The proposed solution, based on Deep Learning, is built on a 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. The proposed fall detection solution enables to detect 86.2\% of falls while producing only 11.6\% of false alarms in average on the considered databases. ## [Slides](https://mycore.core-cloud.net/index.php/s/HBMIFGiPrzbrgRj)