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Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects

Author

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  • Mattia Perrone
  • Steven P. Mell
  • John Martin
  • Shane J. Nho
  • Philip Malloy

Abstract

The application of machine learning in the field of motion capture research is growing rapidly. The purpose of the study is to implement a long-short term memory (LSTM) model able to predict sagittal plane hip joint moment (HJM) across three distinct cohorts (healthy controls, patients and post-operative patients) starting from 3D motion capture and force data. Statistical parametric mapping with paired samples t-test was performed to compare machine learning and inverse dynamics HJM predicted values, with the latter used as gold standard. The results demonstrated favorable model performance on each of the three cohorts, showcasing its ability to successfully generalize predictions across diverse cohorts.

Suggested Citation

  • Mattia Perrone & Steven P. Mell & John Martin & Shane J. Nho & Philip Malloy, 2025. "Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 28(7), pages 1093-1097, May.
    • Handle: RePEc:taf:gcmbxx:v:28:y:2025:i:7:p:1093-1097
    DOI: 10.1080/10255842.2024.2310732
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