UDSP+: stress detection based on user-reported emotional ratings and wearable skin conductance sensor

Detecting stress during user experience (UX) evaluation is particularly important. Studies have shown that skin conductance (SC) is a physiological signal highly associated with stress. This paper investigates how SC Responses (SCRs) can contribute to the development of a publicly available stress detection mechanism. In specific, SCRs located in users' self-reported stress periods were used as a training dataset for the creation of our UDSP+ predictor. A lab study was conducted to evaluate the accuracy of our approach. The SC of 24 participants was recorded using the wearable Nexus10 sensor. Moreover, participants' self-reported emotional ratings (valence-arousal) were obtained using the Affect Grid Tool retrospectively. The performance of the UDSP+ was tested using machine learning. Considering the 2-class classification problem (stress vs. non-stress), an accuracy of up to 86% was achieved. This demonstrates the dynamics of users' self-reported periods to act as a dataset creation mechanism in tow with SCRs.