%0 Conference Proceedings
%T Detection of Subtle Stress Episodes During UX Evaluation: Assessing the Performance of the WESAD Bio-Signals Dataset
%+ University of Peloponnese
%+ Hellenic Open University [Patras]
%+ Aristotle University of Thessaloniki
%A Liapis, Alexandros
%A Faliagka, Evanthia
%A Katsanos, Christos
%A Antonopoulos, Christos
%A Voros, Nikolaos
%Z Part 3: Human-Centered AI
%< avec comité de lecture
%@ 978-3-030-85612-0
%( Lecture Notes in Computer Science
%B 18th IFIP Conference on Human-Computer Interaction (INTERACT)
%C Bari, Italy
%Y Carmelo Ardito
%Y Rosa Lanzilotti
%Y Alessio Malizia
%Y Helen Petrie
%Y Antonio Piccinno
%Y Giuseppe Desolda
%Y Kori Inkpen
%I Springer International Publishing
%3 Human-Computer Interaction – INTERACT 2021
%V LNCS-12934
%N Part III
%P 238-247
%8 2021-08-30
%D 2021
%R 10.1007/978-3-030-85613-7_17
%K Stress detection
%K UX evaluation
%K Bio-signals
%K Electrodermal activity
%Z Computer Science [cs]Conference papers
%X Stress is a highly subjective condition and may largely vary in different contexts. Bio-signals have been widely used by researchers and practitioners to monitor stress levels. Consequently, various bio-signals datasets for stress recognition have been recorded. The most of publicly available physiological datasets have been emotionally annotated in a context where users have been exposed to intense stressors, such as movie clips, songs, major hardware/software failures, image datasets, and gaming. However, it remains unexplored how effectively such datasets can be used in different contexts. This paper investigates the performance of the publicly available dataset named WESAD (Wearable Stress and Affect Detection) in the context of UX evaluation. More specifically, skin conductance signal from WESAD was used to train four machine learning classifiers. Regarding the binary classification problem (stress vs. no stress), models’ accuracy was rather high (at least 91.1%). However, it was found that their effectiveness in assessing stress in the context of UX was rather poor when a new bio-signals dataset was used.
%G English
%Z TC 13
%2 https://inria.hal.science/hal-04292389/document
%2 https://inria.hal.science/hal-04292389/file/520517_1_En_17_Chapter.pdf
%L hal-04292389
%U https://inria.hal.science/hal-04292389
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC13
%~ IFIP-INTERACT
%~ IFIP-LNCS-12934