ReBAT: regression-based anxiety recognition during be-havioural avoidance tasks
Publication date
2025-11-14
Document type
Konferenzbeitrag
Author
Organisational unit
Conference
9th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2025) ; Prague, Czech Republic ; September 19-21, 2025
Publisher
EDP Sciences
Series or journal
BIO Web of Conferences
ISSN
Periodical volume
195
Article ID
01002
Part of the university bibliography
✅
Language
English
Abstract
Behavioural Avoidance Tests (BATs) are commonly used to assess anxiety responses in individuals with specific phobias, but they typically rely on observable behaviour and self-report, offering only momentary insights into anxiety levels. This study investigates the feasibility of continuously estimating anxiety intensity during BATs using physiological data collected from a wrist-worn sensor. Twenty-five participants with spider phobia completed four BATs, both in vivo and virtual reality, in a single session. Using heart rate, heart rate variability, electrodermal activity, and skin temperature, we trained regression models to predict anxiety ratings from three types of input data: (1) only physiological signals, (2) computed features (e.g., min, max, range, variability), and (3) computed features combined with contextual task information. Predictive performance improved with added feature complexity, with the best model achieving a root mean squared error (RMSE) of 0.197 and a mean absolute error (MAE) of 0.041. The results clearly show that wearable sensors can provide meaningful, continuous estimations of anxiety during BATs, which can assist therapists in therapy planning to create more personalised treatment.
Description
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
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Published version
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