DISCOVER: a Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of human behavior
Year: 2025
Type: article
Abstract: 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 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.
Source: Frontiers in Digital Health
Institutions: University of Augsburg, Universität Trier
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,077
Funder: Deutsche Forschungsgemeinschaft
Awards: TherapAI Analyse des nonverbalen Emotionsausdrucks in der Psychotherapie mittels künstlicher Intelligenz, Prospektiv randomisiert kontrollierte Studie zur Entwicklung und Evaluation personalisierter Vorhersagen von frühen Behandlungsstrategien in der ambulanten Psychotherapie depressiver Störungen
OpenAlex ID: W4414342292