@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.}
}
@article {Iazzi2020247,
	title = {Efficient fall activity recognition by combining shape and motion features},
	journal = {Computational Visual Media},
	volume = {6},
	number = {3},
	year = {2020},
	note = {cited By 2},
	pages = {247-263},
	abstract = {This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape variation using three features, the aspect ratio of the bounding box, the orientation of an ellipse representing the body, and the aspect ratio of the projection histogram. For motion variation, we extract several features from three blocks corresponding to the head, center of the body, and feet using optical flow. For each block, we compute the speed and the direction of motion. Each activity is represented by a feature vector constructed from variations in shape and motion features for a set of frames. A support vector machine is used to classify fall and non-fall activities. Experiments on three different datasets show the effectiveness of our proposed method. {\textcopyright} 2020, The Author(s).},
	keywords = {Activity recognition, Aspect ratio, Direction of motion, Feature vectors, Motion variation, Optical flows, Projection histograms, Shape deformation, Shape variations, Support vector machines, Vision based system},
	doi = {10.1007/s41095-020-0183-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090864771\&doi=10.1007\%2fs41095-020-0183-7\&partnerID=40\&md5=21fcdf876b091c6b30486427e7cd9bff},
	author = {Iazzi, A. and Rziza, M. and Thami, R.O.H.}
}
@conference {Iazzi20181,
	title = {Fall detection based on posture analysis and support vector machine},
	booktitle = {2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/ATSIP.2018.8364462},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048484597\&doi=10.1109\%2fATSIP.2018.8364462\&partnerID=40\&md5=d873ad85d14aac2034c2de8077860920},
	author = {Iazzi, A. and Rziza, M. and Thami, R.O.H.}
}
