Stress Detection Using Machine Learning Algorithms

Authors

  • V. R. Archana PG Student, Department of Computer Science and Engineering, RNS Institute of Technology, Bangalore, India
  • B. M. Devaraju Assistant Professor, Department of Computer Science and Engineering, RNS Institute of Technology, Bangalore, India

Keywords:

Electrocardiogram (ECG), Electromyogram (EMG), Galvanic Skin Response (GSR), Heart Rate, Machine Learning, Respiration, Stress

Abstract

Stress is intuited as the most important element in the person life. The World Health Organization is defined as stress is a mental health problem that affects the citizens. In world so many people are suffering from stress. Stress is one of the main symptoms in all the human being for the mental health. Stress can affect all the aspects of our life including our emotions, thinking ability, our behaviour etc. So we have to control the stress. The proposed system we have taken the statistical dataset and also considered the six attributes are Electrocardiogram, Electromyogram, Galvanic Skin Response Hand and Foot, Heart Rate and Respiration. Based on these attributes values we have compared with the threshold values and using machine learning algorithms like decision tree, Naïve-Bayes and K-Nearest Neighbour. We have been used appropriate algorithms to get increased accuracy and predict the stress level.

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Published

15-08-2020

How to Cite

[1]
V. R. Archana and B. M. Devaraju, “Stress Detection Using Machine Learning Algorithms”, IJRESM, vol. 3, no. 8, pp. 251–256, Aug. 2020.

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Section

Articles