A Review on Human Action Recognition and Machine Learning Techniques for Suicide Detection System
| Autor: | V. Rahul Chiranjeevi, D. Elangovan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry 02 engineering and technology Commit Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Svm classifier 0202 electrical engineering electronic engineering information engineering Action recognition 020201 artificial intelligence & image processing Artificial intelligence business Inefficiency computer |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030166809 |
| DOI: | 10.1007/978-3-030-16681-6_5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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