A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Non-intrusive physiological monitoring for automated stress detection in human-computer interaction
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Using galvanic skin response measures to identify areas of frustration for older web 2.0 users
Proceedings of the 2010 International Cross Disciplinary Conference on Web Accessibility (W4A)
Discriminating stress from cognitive load using a wearable EDA device
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
FEEL: frequent EDA and event logging -- a mobile social interaction stress monitoring system
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Monitor and understand pilgrims: data collection using smartphones and wearable devices
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Monitor pilgrims: prayer activity recognition using wearable sensors
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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Nine call center employees wore a skin conductance sensor on the wrist for a week at work and reported stress levels of each call. Although everyone had the same job profile, we found large differences in how individuals reported stress levels, with similarity from day to day within the same participant, but large differences across the participants. We examined two ways to address the individual differences to automatically recognize classes of stressful/non-stressful calls, namely modifying the loss function of Support Vector Machines (SVMs) to adapt to the varying priors, and giving more importance to training samples from the most similar people in terms of their skin conductance lability. We tested the methods on 1500 calls and achieved an accuracy across participants of 78.03% when trained and tested on different days from the same person, and of 73.41% when trained and tested on different people using the proposed adaptations to SVMs.