@article{(Open Science Index):https://publications.waset.org/pdf/10010350,
	  title     = {Deep Learning Based Fall Detection Using Simplified Human Posture},
	  author    = {Kripesh Adhikari and  Hamid Bouchachia and  Hammadi Nait-Charif},
	  country	= {},
	  institution	= {},
	  abstract     = {Falls are one of the major causes of injury and death
among elderly people aged 65 and above. A support system to
identify such kind of abnormal activities have become extremely
important with the increase in ageing population. Pose estimation
is a challenging task and to add more to this, it is even more
challenging when pose estimations are performed on challenging
poses that may occur during fall. Location of the body provides a
clue where the person is at the time of fall. This paper presents
a vision-based tracking strategy where available joints are grouped
into three different feature points depending upon the section they are
located in the body. The three feature points derived from different
joints combinations represents the upper region or head region,
mid-region or torso and lower region or leg region. Tracking is always
challenging when a motion is involved. Hence the idea is to locate
the regions in the body in every frame and consider it as the tracking
strategy. Grouping these joints can be beneficial to achieve a stable
region for tracking. The location of the body parts provides a crucial
information to distinguish normal activities from falls.},
	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {13},
	  number    = {5},
	  year      = {2019},
	  pages     = {255 - 260},
	  ee        = {https://publications.waset.org/pdf/10010350},
	  url   	= {https://publications.waset.org/vol/149},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 149, 2019},