What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data

Authors:
Jorn Bakker;Mykola Pechenizkiy;Natalia Sidorova
Affiliations:
-;-;-
Venue:
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Year:
2011

Citing 0
Cited 8

Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Hybrid genetic algorithms for stress recognition in reading

EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Visualizing and managing stress through colors and images

Proceedings of the 4th International SenseCam & Pervasive Imaging Conference
Optimal time segments for stress detection

MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Modeling stress recognition in typical virtual environments

Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Pervasive and unobtrusive emotion sensing for human mental health

Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Modeling a stress signal

Applied Soft Computing
Modeling observer stress for typical real environments

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. Stress management should start far before the stress start causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. In this paper we (1) formulate the problem of stress identification and categorization from the sensor data stream mining perspective, (2) consider a reductionist approach for arousal identification as a drift detection task, (3) highlight the major problems of dealing with GSR data, collected from a watch-style stress measurement device in normal (i.e. in non-lab) settings, and propose simple approaches how to deal with them, and (4) discuss the lessons learnt from the conducted experimental study on real GSR data collected during the recent field study.