{"url":"/dataset/bah","name":"BAH","full_name":"Behavioural Ambivalence/Hesitancy","description_markdown":"Recognizing complex emotions linked to ambivalence and hesitancy (A/H) can play a critical role in the personalization and effectiveness of digital behaviour change interventions. These subtle and conflicting emotions are manifested by a discord between multiple modalities, such as facial and vocal expressions, and body language. Although experts can be trained to identify A/H, integrating them into digital interventions is costly and less effective. \r\nAutomatic learning systems provide a cost-effective alternative that can adapt to individual users, and operate  seamlessly within real-time, and resource-limited environments. However, there are currently no datasets available for the design of ML models to recognize A/H. \r\n\r\nThis paper introduces a first Behavioural Ambivalence/Hesitancy ( BAH) dataset collected for subject-based multimodal recognition of A/H in videos.  It contains videos from 224 participants captured across 9 provinces in Canada, with different age, and ethnicity. Through our web platform, we recruited participants to answer 7 questions, some of which were designed to elicit A/H while recording themselves via webcam with microphone. BAH amounts to 1,118 videos for a total duration of 8.26 hours with 1.5 hours of A/H.  Our behavioural team annotated timestamp segments to indicate where A/H occurs, and provide frame- and video-level annotations with the A/H cues. Video transcripts and their timestamps are also included, along with cropped and aligned faces in each frame, and a variety of participants meta-data. \r\n\r\nAdditionally, this paper provides preliminary benchmarking results baseline models for BAH at frame- and video-level recognition with mono- and multi-modal setups. It also includes results on models for zero-shot prediction, and for personalization using unsupervised domain adaptation. The limited performance of baseline models highlights the challenges of recognizing A/H in real-world videos. The data, code, and pretrained weights are available.","description_withheld":null,"homepage":"https://github.com/sbelharbi/bah-dataset","introduced_date":"2025-05-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/bah-dataset-for-ambivalence-hesitancy","title":"BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Behavioural Change","first_author":"Manuela González-González","url":null},"license":{"name":"Custom - for research purposes only.","url":"https://www.crhscm.ca/redcap/surveys/?s=LDMDDJR3AT9P37JY"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Video Action Detection","url":"/task/video-action-detection","datasets_with_task":"/datasets/task/video-action-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"French","url":"/datasets/language/french"}],"variants":["BAH"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}