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Activity-Aware Mental Stress Detection Using Physiological Sensors

  • Feng-Tso Sun
  • Cynthia Kuo
  • Heng-Tze Cheng
  • Senaka Buthpitiya
  • Patricia Collins
  • Martin Griss
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 76)

Abstract

Continuous stress monitoring may help users better understand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were limited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring application for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.

We present an activity-aware mental stress detection scheme. Electrocardiogram (ECG), galvanic skin response (GSR), and accelerometer data were gathered from 20 participants across three activities: sitting, standing, and walking. For each activity, we gathered baseline physiological measurements and measurements while users were subjected to mental stressors. The activity information derived from the accelerometer enabled us to achieve 92.4% accuracy of mental stress classification for 10-fold cross validation and 80.9% accuracy for between-subjects classification.

Keywords

Mental stress electrocardiogram galvanic skin response physical activity heart rate variability decision tress Bayes net support vector machine stress classifier 

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Feng-Tso Sun
    • 1
  • Cynthia Kuo
    • 1
    • 2
  • Heng-Tze Cheng
    • 1
  • Senaka Buthpitiya
    • 1
  • Patricia Collins
    • 1
  • Martin Griss
    • 1
  1. 1.Carnegie Mellon UniversityUSA
  2. 2.Nokia Research CenterUSA

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