Stress Detection Using Machine Learning

• Author(s): Sayali Shelke ; Shubhangi Kor ; Sahil Bavaskar ; Kirti Rajadnya
• Paper ID: 1702649
• Page: 38-42
• Published Date: 13-04-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 10 April-2021

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.

Citations

IRE Journals:
Sayali Shelke , Shubhangi Kor , Sahil Bavaskar , Kirti Rajadnya "Stress Detection Using Machine Learning" Iconic Research And Engineering Journals Volume 4 Issue 10 2021 Page 38-42

IEEE:
Sayali Shelke , Shubhangi Kor , Sahil Bavaskar , Kirti Rajadnya "Stress Detection Using Machine Learning" Iconic Research And Engineering Journals, 4(10)