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Stress Detection Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
A In today?s word one of the major leading factors to health problem is STRESS. The basic parameters on which stress can be identified are heart rate, galvanic skin response, body temperature, blood pressure, which provides detailed information of the state of mind of a person. These parameters varying from person to person on the basis of certain things such as their body condition, age and gender. The main goal of the system is to analyze the mental stress through physiological data using electrocardiograph in different positions and moods. Different pre-processing techniques can be used for stress detection. In feature extraction discrete wavelet transform can apply. Many classifiers like artificial neural network, support vector machine, Bayesian network, and decision tree are using to get more accurate results based on accuracy. Physiological sensors analytics is becoming more and more important as the availability of sensor-enabled portable, wearable, and implantable devices becomes ubiquitous in the growing Internet of Things (IOT). Physiological multi-sensor studies have been conducted successfully to detect stress.
Keywords
Stress Detect Parameters, Chest Pain, Rest ECG, Prevention.
References
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[2] Feng-Tso Sun, Cynthia Kuo, Heng-Tze Cheng, Senaka Buthpitiya, Patricia Collins: “Activity Aware Mental Stress Detection Using Physiological Sensors”, Institute for Computer Sciences, Social Informatics and Telecommunications Engineering2012.
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[5] D. F. Dinges, S. Venkataraman, E. L. McGlinchey, and D. N. Metaxas, “Monitoring of facial stress during space flight: Optical computer recognition combining discriminative and generative methods”, in Acta Astronaut., vol. 60, no. 47, pp. 341350, Feb. Apr. 2007,2005.
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[7] Jennifer A. Healey and Rosalind W. Picard, “Detecting Stress During Real World Driving Tasks Using Physiological Sensors”, in IEEE Transactions on Intelligent Transportation systems, vol. 6, no.
How to cite this paper
@article{1702649,
author = {Sayali Shelke, Shubhangi Kor, Sahil Bavaskar, Kirti Rajadnya},
title = {Stress Detection Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {10},
pages = {38-42},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702649.pdf},
abstract = {A In today?s word one of the major leading factors to health problem is STRESS. The basic parameters on which stress can be identified are heart rate, galvanic skin response, body temperature, blood pressure, which provides detailed information of the state of mind of a person. These parameters varying from person to person on the basis of certain things such as their body condition, age and gender. The main goal of the system is to analyze the mental stress through physiological data using electrocardiograph in different positions and moods. Different pre-processing techniques can be used for stress detection. In feature extraction discrete wavelet transform can apply. Many classifiers like artificial neural network, support vector machine, Bayesian network, and decision tree are using to get more accurate results based on accuracy. Physiological sensors analytics is becoming more and more important as the availability of sensor-enabled portable, wearable, and implantable devices becomes ubiquitous in the growing Internet of Things (IOT). Physiological multi-sensor studies have been conducted successfully to detect stress. },
keywords = {Stress Detect Parameters, Chest Pain, Rest ECG, Prevention.},
month = {April},
}