DISCOVER: a data-driven interactive system for comprehensive observation, visualization, and exploration of human behavior

  • Understanding human behavior is a fundamental goal of social sciences, yet conventional methodologies are often limited by labor-intensive data collection and complex analyses. Computational models offer a promising alternative for analyzing large datasets and identifying key behavioral indicators, but their adoption is hindered by technical complexity and substantial computational requirements. To address these barriers, we introduce DISCOVER , a modular and user-friendly software framework designed to streamline computational data exploration for human behavior analysis. DISCOVER democratizes access to state-of-the-art models, enabling researchers across disciplines to conduct detailed behavioral analyses without extensive technical expertise. In this paper, we are showcasing DISCOVER using four modular data exploration workflows that build on each other: Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search. Finally, we report initialUnderstanding human behavior is a fundamental goal of social sciences, yet conventional methodologies are often limited by labor-intensive data collection and complex analyses. Computational models offer a promising alternative for analyzing large datasets and identifying key behavioral indicators, but their adoption is hindered by technical complexity and substantial computational requirements. To address these barriers, we introduce DISCOVER , a modular and user-friendly software framework designed to streamline computational data exploration for human behavior analysis. DISCOVER democratizes access to state-of-the-art models, enabling researchers across disciplines to conduct detailed behavioral analyses without extensive technical expertise. In this paper, we are showcasing DISCOVER using four modular data exploration workflows that build on each other: Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search. Finally, we report initial findings from a user study. The study examined DISCOVER ’s potential to support prospective psychotherapists in structuring information for treatment planning, i.e. case conceptualizations.show moreshow less

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Metadaten
Author:Tobias HallmenORCiDGND, Dominik SchillerORCiDGND, Antonia VehlenORCiD, Steffen Eberhardt, Tobias BaurORCiDGND, Daksitha Withanage Withanage DonGND, Wolfgang LutzORCiD, Elisabeth AndréORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1257567
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/125756
ISSN:2673-253XOPAC
Parent Title (English):Frontiers in Digital Health
Publisher:Frontiers Media S.A.
Place of publication:Lausanne
Type:Article
Language:English
Date of first Publication:2025/09/19
Publishing Institution:Universität Augsburg
Release Date:2025/10/09
Tag:behavioral indicators; computational models; data exploration; human behavior analysis; interactive system; machine learning; multimodal analysis; psychotherapy
Volume:7
First Page:1638539
DOI:https://doi.org/10.3389/fdgth.2025.1638539
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung