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TECHNOLOGY AND CODE article

Front. Digit. Health

Sec. Digital Mental Health

Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1638539

This article is part of the Research TopicEmotional Intelligence AI in Mental HealthView all 9 articles

DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behavior

Provisionally accepted
Tobias HallmenTobias Hallmen1*Dominik SchillerDominik Schiller1*Antonia VehlenAntonia Vehlen2Steffen EberhardtSteffen Eberhardt2Tobias BaurTobias Baur1Daksitha WithanageDaksitha Withanage1Wolfgang LutzWolfgang Lutz2Elisabeth AndréElisabeth André1
  • 1Chair for Human-Centered Artificial Intelligence, University of Augsburg, Augsburg, Germany
  • 2Department of Psychology, Division of Clinical Psychology and Psychotherapy, Universitat Trier, Trier, Germany

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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.

Keywords: Human behavior analysis, Computational models, data exploration, Interactive system, Behavioral indicators, Psychotherapy, machine learning, multimodal analysis

Received: 30 May 2025; Accepted: 29 Aug 2025.

Copyright: © 2025 Hallmen, Schiller, Vehlen, Eberhardt, Baur, Withanage, Lutz and André. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Tobias Hallmen, Chair for Human-Centered Artificial Intelligence, University of Augsburg, Augsburg, Germany
Dominik Schiller, Chair for Human-Centered Artificial Intelligence, University of Augsburg, Augsburg, Germany

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material