Generative deep learning has emerged as a promising data augmentation technique in recent years. This approach becomes particularly valuable in areas such as motion analysis, where it is challenging to collect substantial amounts of data. The current study introduces a data augmentation strategy that relies on a variational autoencoder to generate synthetic data of kinetic and kinematic variables. The kinematic and kinetic variables consist of hip and knee joint angles and moments, respectively, in both sagittal and frontal plane, and ground reaction forces. Statistical parametric mapping (SPM) did not detect significant differences between real and synthetic data for each of the biomechanical variables considered. To further evaluate the effectiveness of this approach, a long-short term model (LSTM) was trained both only on real data (R) and on the combination of real and synthetic data (R&S); the performance of each of these two trained models was then assessed on real test data unseen during training. The predictive model achieved comparable results in terms of nRMSE when predicting knee joint moments in the frontal (R&S: 9.86% vs R:10.72%) and sagittal plane (R&S: 9.21% vs R: 9.75%), and hip joint moments in the frontal (R&S: 16.93% vs R:16.79%) and sagittal plane (R&S: 13.29% vs R:14.60%). These findings suggest that the proposed methodology is an effective data augmentation approach in motion analysis settings.

We disclose the following conflicts of interest: Mattia Perrone (none), Steven P. Mell (none), John Martin (none), Scott Simmons (none), Philip Malloy (none), Shane J. Nho (Allosource: Research support, American Orthopaedic Association: Board or committee member, American Orthopaedic Society for Sports Medicine: Board or committee member, Arthrex, Inc: Research support, Arthroscopy of North America: Board or committee member, Athletico: Research support, DJ Orthopaedics: Research support, Linvatec: Research support, Miomed: Research support, Ossur: IP royalties, Smith Nephew: Research support, Springer: Publishing royalties, financial or material support, Stryker: IP royalties; Paid consultant; Research support).

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The IRB of Rush University gave full ethical approval for this study

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All data produced in the present study are available upon reasonable request to the authors