@article{ref1,
title="Detecting falls and estimation of daily habits with depth images using machine learning algorithms",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2020",
author="Msaad, Soumaya and Cormier, Geoffroy and Carrault, Guy",
volume="2020",
number="",
pages="2163-2166",
abstract="Different approaches have been proposed in the literature to detect the fall of an elderly person. In this paper, we propose a fall detection method based on the classification of parameters extracted from depth images. Three supervised learning methods are compared: decision tree, K-Nearest Neighbors (K-NN) and Random Forests (RF). The methods have been tested on a database of depth images recorded in a nursing home over a period of 43 days. The Random Forests based method yields the best results, achieving 93% sensitivity and 100% specificity when we restrict our study around the bed. Furthermore, this paper also proposes a 37 days follow-up of the person, to try and estimate his or her daily habits.<p /> <p>Language: en</p>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC44109.2020.9175601",
url="http://dx.doi.org/10.1109/EMBC44109.2020.9175601"
}