In this study, we aim to determine the stress level in a non-invasive way to the maximum extent possible by analyzing behavioral and contextual data received from the only source being a smartphone containing the data gathered in real-life situations. The information collected includes audio, gyroscope and accelerometer features, light condition, screen mode (on/off), current stress level self-assessment, and the current activity type. Three stress analysis models have been built: two with the consideration of current activities of a participant and one without those. Classification of low- and high-stress conditions, which was executed for a separate model for a certain kind of activity only, enabled us to achieve 3.9 % higher accuracy than that under the conditions when those activities were neglected. Also, the Android application was developed as a means for the current activity-type identification.
SYSOEV, Mikhail, KOS, Andrej, POGAČNIK, Matevž. Noninvasive stress recognition considering the current activity. Personal and ubiquitous computing, ISSN 1617-4909, Oct. 2015, vol. 19, no. 7, str. 1045-1052, ilustr. http://link.springer.com/article/10.1007/s00779-015-0885-5, doi: 10.1007/s00779-015-0885-5.