@article{ref1,
title="Fall detector adapted to nursing home needs through an optical-flow based CNN",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2020",
author="Carlier, Alexy and Peyramaure, Paul and Favre, Ketty and Pressigout, Muriel",
volume="2020",
number="",
pages="5741-5744",
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 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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC44109.2020.9175844",
url="http://dx.doi.org/10.1109/EMBC44109.2020.9175844"
}