Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML model...

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Veröffentlicht in:Frontiers in bioengineering and biotechnology Jg. 12; S. 1350135
Hauptverfasser: Dindorf, Carlo, Dully, Jonas, Konradi, Jürgen, Wolf, Claudia, Becker, Stephan, Simon, Steven, Huthwelker, Janine, Werthmann, Frederike, Kniepert, Johanna, Drees, Philipp, Betz, Ulrich, Fröhlich, Michael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media SA 14.02.2024
Frontiers Media S.A
Schlagworte:
Accuracy
Artificial intelligence
Asymptomatic
Bioengineering and Biotechnology
Biomechanics
Data analysis
data augmentation
Data collection
Datasets
Deep learning
Feasibility studies
Gait
Generative artificial intelligence
Human mechanics
Kinematics
Learning algorithms
Machine learning
Posture
spine
statistical parametric mapping
variational autoencoder
Germany
deep learning
spine
data augmentation
machine learning
variational autoencoder
statistical parametric mapping
ISSN:2296-4185, 2296-4185
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Zusammenfassung:Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.
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Edited by: Zhen (Jeff) Luo, University of Technology Sydney, Australia
Reviewed by: Tianzhe Bao, University of Health and Rehabilitation Sciences, China
These authors have contributed equally to this work and share senior authorship
Chang Won Jeong, Wonkwang University, Republic of Korea
ISSN:2296-4185
2296-4185
DOI:10.3389/fbioe.2024.1350135