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Prediction of fight or flight response using artificial neural networks

Publication Type : Journal Article

Publisher : American Journal of Applied Sciences

Source : American Journal of Applied Sciences, Science Publications, Volume 11, Issue 6, p.912-920 (2014)

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Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2014

Abstract : The modern society has posed several threats to the public. Public security is declining with increasing anti-social behaviour. Cases of rape and terrorist attacks have become increasingly common and there is a strong demand for a security system to control such modalities. Anti-social behaviour is a key issue of public concern. Public perceptions, however, have been improving recently. The vital response to physical and emotional danger is called fight or flight response. It is a basic survival mechanism occurring in response to a specific stimulus, such as pain or the threat of danger. Predicting the flight and fight response is an important aspect to identify possible areas susceptible to such events and provide emergency assistance to the victims involved. This study analyses various physiological changes associated with fight or flight response and proposes an approach to predict measures that determines whether an individual is under fear caused due the perceived threat. The proposed approach uses feed forward neural networks with back propagation algorithm. With the physiological changes such as blood pressure, heart rate and respiratory rate as inputs, the optimal configuration of neural network was configured and the proposed system is able to predict the measure of fight or flight response with minimal error. By monitoring and identifying the fear measure it is possible to prevent or reduce the damage to the society by activities such as rape and terrorist attacks.

Cite this Research Publication : A. Suresh, Latha, S. S., Prem, N., and Dr. Radhika N., “Prediction of fight or flight response using artificial neural networks”, American Journal of Applied Sciences, vol. 11, no. 6, pp. 912-920, 2014.

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