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Everyday Activity Science and Engineering Table Setting Dataset
Moritz Meier1, Yale Hartmann1, Yasmina El Ouahabi1
1University Bremen, Cognitive Systems Lab (CSL), Bremen, 28359, Germany.
Scientific Data
|May 12, 2026
Summary
This study introduces the Everyday Activity Science and Engineering Table Setting Dataset (EASE-TSD), a rich multimodal biosignal resource. It enables research into human activity planning and execution for advanced robotics and cognitive systems.
Area of Science:
- Cognitive Science
- Robotics
- Human-Computer Interaction
- Biomedical Engineering
Background:
- Understanding human activity planning is vital for developing intelligent systems.
- Existing datasets often lack multimodal biosignal richness for complex task analysis.
Purpose of the Study:
- To introduce and validate the Everyday Activity Science and Engineering Table Setting Dataset (EASE-TSD).
- To provide a comprehensive multimodal dataset for studying human activity planning and execution.
Main Methods:
- Collected multimodal biosignals (motion capture, video, eye-tracking, EMG, EDA, EEG, etc.) from 78 participants setting a table.
- Synchronously recorded eight high-dimensional biosignal streams.
- Annotated data with activity phases, actions, motions, objects, and think-aloud protocols.
Main Results:
- The EASE-TSD dataset comprises 78 sessions with six table-setting trials each.
- Data includes synchronized motion capture, video, eye-tracking, EMG, EDA, EEG, and audio.
- Comprehensive 3-level annotations and think-aloud protocols are provided.
Conclusions:
- EASE-TSD offers a valuable resource for advancing research in human activity understanding.
- The dataset facilitates the development of cognition-enabled robots and AI systems.
- Leverages advanced biosignal processing and machine learning for analysis.
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