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DISCOVER: a Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of human
Tobias Hallmen1, Dominik Schiller1, Antonia Vehlen2
1Chair for Human-Centered Artificial Intelligence, University of Augsburg, Augsburg, Germany.
Frontiers in Digital Health
|October 6, 2025
Summary
Researchers can now analyze human behavior more easily using DISCOVER, a new software framework. It simplifies complex computational models, making advanced data exploration accessible to more scientists.
Area of Science:
- Social Sciences
- Computational Social Science
- Human Behavior Analysis
Background:
- Traditional human behavior analysis methods are labor-intensive and complex.
- Computational models offer advanced analysis but face barriers in technical complexity and resource needs.
Purpose of the Study:
- Introduce DISCOVER, a user-friendly software framework to simplify computational data exploration for human behavior analysis.
- Democratize access to advanced computational models for researchers across disciplines.
- Enable detailed behavioral analyses without extensive technical expertise.
Main Methods:
- Developed DISCOVER, a modular software framework.
- Implemented four integrated data exploration workflows: Semantic Content Exploration, Visual Inspection, Aided Annotation, and Multimodal Scene Search.
- Conducted a user study to evaluate DISCOVER's utility.
Main Results:
- DISCOVER streamlines computational data exploration for human behavior analysis.
- The software framework enables researchers to conduct detailed analyses with less technical expertise.
- Initial user study indicates potential for supporting psychotherapists in treatment planning.
Conclusions:
- DISCOVER lowers barriers to entry for computational human behavior analysis.
- The framework facilitates interdisciplinary research and application of advanced analytical models.
- DISCOVER shows promise in practical applications such as clinical psychology treatment planning.
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