Abstract
Continuous stress monitoring may help users better under- stand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were lim- ited 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 appli- cation 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. Electrocar- diogram (ECG), galvanic skin response (GSR), and accelerometer data were gathered from 20 participants across three activities: sitting, stand- ing, 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 en- abled us to achieve 92.4% accuracy of mental stress classification for 10-fold cross validation and 80.9% accuracy for between-subjects classi- fication.
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Sun, FT., Kuo, C., Cheng, HT., Buthpitiya, S., Collins, P., Griss, M. (2012). Activity-Aware Mental Stress Detection Using Physiological Sensors. In: Gris, M., Yang, G. (eds) Mobile Computing, Applications, and Services. MobiCASE 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29336-8_16
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DOI: https://doi.org/10.1007/978-3-642-29336-8_16
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