Human Stress Indicator Using Machine LearningTechnique and Exhortation based on Health Parameters

Vinutha D; Dr. Nirmala S 1

1

Publication Date: 2024/09/04

Abstract: Stress, often known as stressors, is a psychological or emotional state brought on by difficult or inevitable situations. Understanding human stress levels is vital to preventing negative life experiences. There may be connections between sleep-related difficulties and a range of psychological, social, and medical conditions. The aim is to look into the empirical identification of human stress levels by applying algorithmic techniques with health data. After data pre- processing, a few algorithmic approaches were utilized to assess stress levels, which were categorized from low to high: Multilayer Perception, Random Forest, Support Vector Machine, Decision Trees, Na ̈ıve Bayes, and Logistic Regression. This strategy made it possible to compare methods and find the most precised one.

Keywords: Naive Bayes Classifier, SVM, AdaBoost Classi- Fier, Stress Predictor, Data Analysis, MLP Classifier.

DOI: https://doi.org/10.38124/ijisrt/IJISRT24AUG1351

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24AUG1351.pdf

REFERENCES

  1. Human Stress Detection In And Through Sleep By Using Machine Learning: Y Peer Mohideen, A Aney, A Reji Princy, M Suvathi
  2. Remote Detection and Classification of Human Stress Using a Depth Sensing Technique: Yuhao Shan, Tong Chen, Liansheng Yao, Zhan wu, Wanhui Wen, Guangyuan Liu
  3. A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques: Shruthi Gedam, Sanchita Paul.
  4. An Integrated Human Stress Detection Sensor Using Supervised Algorithms: Amirmohammad Mohammadi, Mohammad Fakharzadehm
  5. Detection of Psychological Stress Using a Hyperspectral Imaging Technique: Tong Chen, Peter Yuen, Mark Richardson, Guangyuan Liu, and Zhishun She
  6. Effects of Daily Stress in Mental State Classification: Soyeon Park, Suh-Yeon Dong
  7. fNIRS Evidence for Disti nguishing Patients With Major Depression and Healthy Controls: Jinlong Chao, Shuzhen Zheng, Dixin Wang, Xuan Zhang
  8. Stress Detection Using Eye Tracking Data: An Evaluation of Full Parameters: Mansoureh Seyed Yousefi, Farnoush Reisi, Mohammad Reza Daliri, And Vahid Shalchyan
  9. A Wearable EEG Instrument for Real-Time Frontal Asymmetry Monitoring in Worker Stress Analysis: Pasquale Arpaia, Nicola Maccaldi, Roberto Prevete, Isabella Sannino, and Annarita Tedsco
  10. Toward Stress Detection During Gameplay: A Survey : Chamila Wijiyarathna and Erandi Lakshika