Abstract
Most of the existing stress assessment frameworks rely on physiological signals measurements (EEG, ECG, GSR, ST, etc.), which involve direct physical contact with the patient in a medical setup. Present technologies rely on capturing moods and emotions through remote devices (cameras), further processed by computer vision and machine learning techniques. The proposed work describes a method of automatic stress classification where stress information is modeled based on pupil diameter non-intrusive measurements, recorded by an eye tracking remote system. The signal extracted from the pupil Dataset has been processed using the Bag-Of-Words model, with a SVM classification and results have been compared to similar experiments in order to validate the applicability and consistency of the Bag-Of-Words model on stress assessment and classification.
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The present work has been conducted under the 29667 Internal Grant 2014–2015, in the Technical University of Cluj-Napoca, Romania.
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Ciupe, A., Florea, C., Orza, B., Vlaicu, A., Petrovan, B. (2016). A Bag of Words Model for Improving Automatic Stress Classification. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_33
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