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Analysis on Machine Learning Techniques for Stress Detection in Employees

EasyChair Preprint no. 5367

5 pagesDate: April 24, 2021


Mental stress is a common and major issue nowadays especially among working professional, because employees have family commitments with their over workload, target, achievements, etc. Stress tends various health issues like heart attack, stroke, depression, and suicide. Mental stress is not only in employees even normal people also face this problem but the employees has so many stress management techniques to manage the stress like yoga, meditation etc., but still employees suffer from the stress. Stress calculated by the Traditional stress detection method has two types of physiological parameters one is questionnaire format and another one is physiological signals based on Heart rate variability, galvanic skin response, BP, and electrocardiography, etc., Machine learning techniques are applied to analyze and anticipate stress in employees. In this paper, we mainly focus on different machine learning techniques and physiological parameters for stress detection.

Keyphrases: 1. Stress detection, 2. Physiological parameters, 3. Machine Learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {B K Kiranashree and V Ambika and A D Radhika},
  title = {Analysis on Machine Learning Techniques for Stress Detection in Employees},
  howpublished = {EasyChair Preprint no. 5367},

  year = {EasyChair, 2021}}
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