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

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 28; no. 7; pp. 1093 - 1097
• Main Authors: Perrone, Mattia, Mell, Steven P., Martin, John, Nho, Shane J., Malloy, Philip
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 19.05.2025
• Subjects: Adult; Biomechanical Phenomena; deep learning; Female; Hip Joint - physiology; Hip Joint - physiopathology; Hip Joint - surgery; hip joint moment; Humans; long short-term memory model; Machine Learning; Male; Middle Aged; motion analysis; Postoperative Period; deep learning; long short-term memory model; motion analysis; Machine learning; hip joint moment
• ISSN: 1025-5842; 1476-8259; 1476-8259
• DOI: 10.1080/10255842.2024.2310732

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.