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A Review on Human Action Recognition and Machine Learning Techniques for Suicide Detection System

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Innovations in Bio-Inspired Computing and Applications (IBICA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 939))

Abstract

In current world about 800,000 people commit suicide every year. Mortality rate is increasing due to stress and depression. There are various types of suicide out of which hanging is the most common way of death. Though various systems are available for detecting hanging attempts their limitations results in inefficiency of the system. Numerous technologies are evolving everyday out of which an advanced system to detect hanging attempt can be established. This paper provides a comprehensive survey of human action recognition, machine learning techniques and various suicides prevention methods through which hanging attempts can be detected. Finally, an accuracy of various machine learning and human action recognition approaches is described.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India

    V. Rahul Chiranjeevi & D. Elangovan

Authors
  1. V. Rahul Chiranjeevi
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  2. D. Elangovan
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Corresponding authors

Correspondence to V. Rahul Chiranjeevi or D. Elangovan .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  3. Department of Applied Science and Engineering, Indian Institute of Technology, Roorkee, India

    Millie Pant

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Chiranjeevi, V.R., Elangovan, D. (2019). A Review on Human Action Recognition and Machine Learning Techniques for Suicide Detection System. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_5

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