User Experience Evaluation: A Validation Study of a Tool-based Approach for Automatic Stress Detection Using Physiological Signals

ABSTRACT

HCI researchers and practitioners are increasingly using physiological data to measure User eXperience (UX) parameters. The dynamic nature of physiological data offers a continuous window for an in-depth understanding of users’ interaction experience. However, in order to be truly informative, physiological signals need to be linked to users’ interaction experience aspects, such as their emotional states, in a systematic and efficient way. Studies have shown that skin conductance is a physiological signal highly associated with stress. The main purpose of this paper is to present the validation study of our proposed stress detection mechanism which is integrated into a software named PhysiOBS. PhysiOBS is an observation analysis tool that can be used in the post-study analysis phase. PhysiOBS uses nonspecific skin conductance responses (NS-SCRs) in order to auto-report time periods that are probably associated with a problematic interaction. PhysiOBS can also combine multiple data sources. Hence, UX evaluators are able to further investigate a recorded session in order to reveal additional interaction flaws. The integrated stress assessment mechanism, which uses four trained classifiers, can be applied in the reported periods (auto/expert-reported) in order to classify them as stress or non-stress. For the purpose of the validation study, 24 users were recruited in order to participate in a lab experiment. Results showed that our stress assessment mechanism supports UX evaluators by accurately identifying stressful regions within an interaction scenario.

Notes

1. https://www.mindmedia.com/en/products/nexus-10-mkii/

2. https://www.noldus.com/observer-xt

3. https://www.tobiipro.com/product-listing/tobii-pro-lab/

4. https://www.transana.com/

Additional information

Notes on contributors

Alexandros Liapis

Alexandros Liapis holds a PhD in the HCI from the department of Informatics of the Hellenic Open University. He is currently working in the Internal Assessment and Training Unit of the HOU. His current research interests include HCI, User eXperience (UX) evaluation, signal analysis, machine learning and deep learning.

Christos Katsanos

Christos Katsanos is an Assistant Professor of HCI, Department of Informatics, Aristotle University of Thessaloniki, Greece. He holds a PhD (2010) from the Electrical and Computer Engineering Department, University of Patras, Greece. He has 707 known citations and h-index=15. His research interests include HCI, HRI, IA, accessibility and educational technologies.

Nikos Karousos

Nikos Karousos holds a PhD diploma from the Computer Engineering & Informatics Department of University of Patras. His PhD thesis concerns the “Support of Service-Oriented Web Applications Development: an Open Hypermedia System approach”. His research is focused on Hypertext, Service Oriented Architecture, Application Development, Design of Knowledge Management Systems and Software Evaluation.

Michalis Xenos

Michalis Xenos is a professor at Computer Engineering and Informatics Department of University of Patras. He has participated in over 50 research and development projects in the area of software engineering and educational technologies. His current research interests include, inter alia, Software Quality, HCI, HRI and Educational Technologies.

Theofanis Orphanoudakis

Theophanis Orphanoudakis is Associate Professor at the School of Science and Technology of the Hellenic Open University, Director of the Educational Content, Methodology and Technology Laboratory and Director of the Digital Systems and Media Computing Laboratory of the School of Sciences and Technology of the Hellenic Open University.