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A GenAI Pipeline for Violinist Kinematic Data Management
Paria Samimisabet1, Karsten Morisse1
1Faculty of Engineering and Computer Science, University of Applied Sciences, Osnabrück, Germany.
Studies in Health Technology and Informatics
|July 3, 2026
Summary
Generative Artificial Intelligence (GenAI) can effectively manage complex biomechanical health data. A GenAI pipeline accurately reconstructed missing data in a violinists' kinematic dataset, proving its utility in data curation.
Area of Science:
- Biomedical Engineering
- Health Informatics
- Artificial Intelligence
Background:
- Managing complex biomechanical datasets is crucial for digital health research.
- Data cleaning, harmonization, and preparation are significant challenges.
Purpose of the Study:
- To present a Generative Artificial Intelligence (GenAI)-supported pipeline for managing upper-body kinematic data.
- To validate the pipeline's effectiveness in data curation and completion.
Main Methods:
- Developed a GenAI pipeline for dataset profiling, cleaning, schema harmonization, and metadata support.
- Conducted a controlled masking experiment on a kinematic dataset from 26 violinists.
- Reconstructed 200 manually removed data cells using the GenAI pipeline.
Main Results:
- The GenAI pipeline achieved exact agreement with original reference values for all reconstructed numeric and non-numeric fields.
- Demonstrated proof-of-concept for GenAI in controlled data completion for specialized biomechanical datasets.
Conclusions:
- GenAI is a practical tool for health data curation and managing specialized biomechanical datasets.
- GenAI can aid in preparing analysis-ready outputs for digital health research.
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