@article {Iazzi2021,
	title = {Fall detection system-based posture-recognition for indoor environments},
	journal = {Journal of Imaging},
	volume = {7},
	number = {3},
	year = {2021},
	note = {cited By 7},
	abstract = {The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection. Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).},
	keywords = {Background subtraction, Classification (of information), Detection system, Fall detection, Features extraction, Head injuries, Human posture recognition, Indoor environment, Posture recognition, Serious injuries, Support vector machines, Video surveillance},
	doi = {10.3390/jimaging7030042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104580792\&doi=10.3390\%2fjimaging7030042\&partnerID=40\&md5=cb6ec76b0560734c5d22b49662b3876b},
	author = {Iazzi, A. and Rziza, M. and Haj Thami, R.O.}
}
