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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.
Bakker et al. (Thu,) studied this question.