Description of spine motion during gait in normal adolescents and young adults.

Journal and index pages often block iframe embedding. This reader keeps the evidence details in Orthonotes and leaves the source page one click away.

Abstract

[Indexed for MEDLINE] 20. Int Orthop. 2023 Apr;47(4):921-928. doi: 10.1007/s00264-022-05670-0. Epub 2023 Jan 10. Do we still need to screen our patients?-Orthopaedic scoring based on motion tracking. Raab D(1), Heitzer F(2), Liaw JC(3), Müller K(3), Weber L(2), Flores FG(3), Kecskeméthy A(3), Mayer C(4), Jäger M(4). Author information: (1)Chair of Mechanics and Robotics, University of Duisburg-Essen, Lotharstraße 1, 47057, Duisburg, Germany. dominik.raab@uni-due.de. (2)Chair of Orthopaedics and Trauma Surgery, University of Duisburg-Essen, Essen, Germany. (3)Chair of Mechanics and Robotics, University of Duisburg-Essen, Lotharstraße 1, 47057, Duisburg, Germany. (4)Department of Orthopaedics, Trauma and Reconstructive Surgery, St. Marien-Hospital Mülheim an der Ruhr, Mülheim an der Ruhr, Germany. PURPOSE: Orthopaedic scores are essential for the clinical assessment of movement disorders but require an experienced clinician for the manual scoring. Wearable systems are taking root in the medical field and offer a possibility for the convenient collection of motion tracking data. The purpose of this work is to demonstrate the feasibility of automated orthopaedic scorings based on motion tracking data using the Harris Hip Score and the Knee Society Score as examples. METHODS: Seventy-eight patients received a clinical examination and an instrumental gait analysis after hip or knee arthroplasty. Seven hundred forty-four gait features were extracted from each patient's representative gait cycle. For each score, a hierarchical multiple regression analysis was conducted with a subsequent tenfold cross-validation. A data split of 70%/30% was applied for training/testing. RESULTS: Both scores can be reproduced with excellent coefficients of determination R2 for training, testing and cross-validation by applying regression models based on four to six features from instrumental gait analysis as well as the patient-reported parameter 'pain' as an offset factor. CONCLUSION: Computing established orthopaedic scores based on motion tracking data yields an automated evaluation of a joint function at the hip and knee which is suitable for direct clinical interpretation. In combination with novel technologies for wearable data collection, these computations can support healthcare staff with objective and telemedical applicable scorings for a large number of patients without the need for trained clinicians. © 2023. The Author(s). DOI: 10.1007/s00264-022-05670-0 PMCID: PMC10014817