@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.}
}
