[2505.19328] BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change

Computer Science > Computer Vision and Pattern Recognition arXiv:2505.19328 (cs) [Submitted on 25 May 2025 (v1), last revised 4 Mar 2026 (this version, v5)] Title:BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change Authors:Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan, Masoumeh Sharafi, Muhammad Haseeb Aslam, Marco Pedersoli, Alessandro Lameiras Koerich, Simon L Bacon, Eric Granger View a PDF of the paper titled BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change, by Manuela Gonz\'alez-Gonz\'alez and 8 other authors View PDF Abstract:Ambivalence and hesitancy (A/H), closely related constructs, are the primary reasons why individuals delay, avoid, or abandon health behaviour changes. They are subtle and conflicting emotions that sets a person in a state between positive and negative orientations, or between acceptance and refusal to do something. They manifest as a discord in affect between multiple modalities or within a modality, such as facial and vocal expressions, and body language. Although experts can be trained to recognize A/H as done for in-person interactions, integrating them into digital health interventions is costly and less effective. Automatic A/H recognition is therefore critical for the personalization and cost-effectiveness of digital behaviour change interventions. However, no datasets currently exist for the design of machine learning models to recogniz...