@article {Alaoui2021,
	title = {Fall detection of elderly people using the manifold of positive semidefinite matrices},
	journal = {Journal of Imaging},
	volume = {7},
	number = {7},
	year = {2021},
	note = {cited By 3},
	abstract = {Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Causes of death, Computer vision, Dynamic time warping, Elderly people, Elderly populations, Fall detection, Gram matrices, Health care, Health risks, Healthcare, Musculoskeletal system, Positive semidefinite matrices, Riemann Manifold, Similarity measure, Support vector machines},
	doi = {10.3390/jimaging7070109},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111011519\&doi=10.3390\%2fjimaging7070109\&partnerID=40\&md5=fe18155309e5ff7e5a54dff191d8c05e},
	author = {Alaoui, A.Y. and Tabii, Y. and Thami, R.O.H. and Daoudi, M. and Berretti, S. and Pala, P.}
}
@conference {Oumaima2021,
	title = {Vision-based fall detection and prevention for the elderly people: A review ongoing research},
	booktitle = {5th International Conference on Intelligent Computing in Data Sciences, ICDS 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Falls are the most leading cause of accidental injury deaths worldwide, therefore, it poses a real challenge for the prevention of life-threatening conditions in geriatrics. The most damaged community is the ever-growing aging population. For this reason, there are considerable demands to distinguish a dangerous posture such as fall in real-time. Here we provide a literature review of conducted work on elderly fall detection and prediction mentioning the main methods to recognize human posture including computer vision-based and wearable sensor-based. We approached these perspectives: sensor fusion, Datasets, approaches proposition. In conclusion, our survey summarizes the progress achieved in the five past years to help the researchers in this field to spot areas where further effort would be beneficial and innovative. {\textcopyright} 2021 IEEE.},
	keywords = {Accident prevention, Accidental injuries, Computer vision, Dataset, Elderly, Elderly people, Fall detection, Fall prediction, Fall prevention, Life threatening conditions, Sensor, Vision based, Wearable sensors},
	doi = {10.1109/ICDS53782.2021.9626736},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123493902\&doi=10.1109\%2fICDS53782.2021.9626736\&partnerID=40\&md5=c99b20a9a8a522cc9440679b23da4ceb},
	author = {Oumaima, G. and Hamd, A.A. and Youness, T. and Rachid, O.H.T. and Omar, B.}
}
