Stress Detection in Working People

S. Sriramprakash · O.V. Ramana Murthy

177 citations0/0 files storedProcedia Computer Science

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Emotion and Mood Recognition Heart Rate Variability and Autonomic Control ECG Monitoring and Analysis

Abstract

Stress detector classifies a stressed individual from a normal one by acquiring his/her physiological signals through appropriate sensors such as Electrocardiogram (ECG), Galvanic Skin Response (GSR) etc,. These signals are pre-processed to extract the desired features which depicts the stress level in working individuals. Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) are investigated to classify these extracted feature set. The result indicates feature vector with best features having a strong influence in stress identification. An attempt is made to determine the best feature set that results in maximum classification accuracy. Proposed techniques are applied on benchmark SWELL-KW dataset and state-of-art results are obtained.

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References

2013World Health Report 2012A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee 2014The SWELL Knowledge Work Dataset for Stress and User Modeling Research 2016Continuous stress detection using a wrist device 2016Detecting Work Stress in Offices by Combining Unobtrusive Sensors

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