@article{(Open Science Index):https://publications.waset.org/pdf/10012744,
	  title     = {Automatic Detection of Suicidal Behaviors Using an RGB-D Camera: Azure Kinect},
	  author    = {Maha Jazouli},
	  country	= {},
	  institution	= {},
	  abstract     = {Suicide is one of the leading causes of death among prisoners, both in Canada and internationally. In recent years, rates of attempts of suicide and self-harm suicide have increased, with hangings being the most frequently used method. The objective of this article is to propose a method to automatically detect suicidal behaviors in real time. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Tests show that the proposed system gives satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {16},
	  number    = {10},
	  year      = {2022},
	  pages     = {508 - 512},
	  ee        = {https://publications.waset.org/pdf/10012744},
	  url   	= {https://publications.waset.org/vol/190},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 190, 2022},
	}