Call Center Stress Recognition with Person-Specific Models
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.
KeywordsStress recognition skin conductance interpersonal variability Support Vector Machines Affective Computing
Unable to display preview. Download preview PDF.
- 1.Barreto, A., Zhai, J., Adjouadi, M.: Non-intrusive physiological monitoring for automated stress detection in human-computer interaction. In: ICCV-HCI, pp. 29–38 (2007)Google Scholar
- 2.Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Annual ACM workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)Google Scholar
- 4.Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), software http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 7.Huang, Y.M., Du, S.X.: Weighted support vector machine for classification with uneven training class sizes. In: 4th International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4365–4369. IEEE Press, Los Alamitos (2005)Google Scholar
- 8.Cacioppo, J.T., Tassinary, L.G., Berntson, G.G.: Handbook of Psychophysiology. Cambridge University Press, Cambridge (2000)Google Scholar
- 10.Lunn, D., Harper, S.: Using galvanic skin response measures to identify areas of frustration for older web 2.0 users. In: International Cross Disciplinary Conference on Web Accessibility, p. 34. ACM, New York (2010)Google Scholar
- 15.Shi, Y., Nguyen, M.H., Blitz, P., French, B., Fisk, S., De la Torre, F., Smailagic, A., Siewiorek, D.P., al’ Absi, M., Ertin, E., Kamarck, T., Kumar, S.: Personalized stress detection from physiological measurements. In: International Symposium on Quality of Life Technology (2010)Google Scholar