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

[2505.19328] BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2505.19328 (cs) [Submitted on 25 May 2025 ( v1 ), last revised 14 Apr 2026 (this version, v7)] 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 recognize A/H. This paper introduces the Behavioural Ambivalence/Hesitancy (BAH) dataset collected for multimodal recognition of A/H in videos. It contains 1,427 videos with a total duration of 10.60 hours, captured from 300 participants across Canada, answering predefined questions to elicit A/H. It is intended to mirror real-world digital behaviour change interventions delivered online. BAH is annotated by three experts to provide timestamps that indicate where A/H occurs, and frame- and video-level annotations with A/H cues. Video transcripts, cropped and aligned faces, and participant metadata are also provided. Since A and H manifest similarly in practice, we provide a binary annotation indicating the presence or absence of A/H. Additionally, this paper includes benchmarking results using baseline models on BAH for frame- and video-level recognition, and different learning setups. The limited performance highlights the need for adapted multimodal and spatio-temporal models for A/H recognition. The data and code are publicly available. Comments: 46 pages, 21 figures, ICLR 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG) Cite as: arXiv:2505.19328 [cs.CV] (or arXiv:2505.19328v7 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2505.19328 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Soufiane Belharbi [ view email ] [v1] Sun, 25 May 2025 21:29:00 UTC (11,922 KB) [v2] Thu, 29 May 2025 11:19:26 UTC (11,932 KB) [v3] Thu, 22 Jan 2026 18:06:39 UTC (18,916 KB) [v4] Mon, 26 Jan 2026 18:01:53 UTC (18,916 KB) [v5] Wed, 4 Mar 2026 10:46:18 UTC (18,917 KB) [v6] Fri, 27 Mar 2026 10:47:00 UTC (18,917 KB) [v7] Tue, 14 Apr 2026 11:07:41 UTC (18,917 KB) Full-text links: Access Paper: 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 TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-05 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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