Machine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy

• Verfasst von: Özateş, Mustafa Erkam [VerfasserIn]
• Verfasst von: Karabulut, Derya [VerfasserIn]
• Verfasst von: Salami, Firooz [VerfasserIn]
• Verfasst von: Wolf, Sebastian Immanuel [VerfasserIn]
• Verfasst von: Arslan, Yunus Ziya [VerfasserIn]
• Titel: Machine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy
• Verf.angabe: Mustafa Erkam Ozates, Derya Karabulut, Firooz Salami, Sebastian Immanuel Wolf, Yunus Ziya Arslan
• E-Jahr: 2023
• Jahr: 3 June 2023
• Umfang: 9 S.
• Fussnoten: Online verfügbar 27 May 2023, Version des Artikels 3 June 2023 ; Gesehen am 20.09.2023
• Titel Quelle: Enthalten in: Journal of biomechanics
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1968
• Jahr Quelle: 2023
• Band/Heft Quelle: 155(2023) vom: Juni, Artikel-ID 111668, Seite 1-9
• ISSN Quelle: 1873-2380
• DOI: doi:10.1016/j.jbiomech.2023.111668
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Cerebral palsy
• Sach-SW: Computational gait analysis
• Sach-SW: Convolutional neural networks
• Sach-SW: Gait kinematics
• Sach-SW: Joint moments
• Sach-SW: Machine Learning
• K10plus-PPN: 1860048471

Abstract

Joint moments during gait provide valuable information for clinical decision-making in patients with cerebral palsy (CP). Joint moments are calculated based on ground reaction forces (GRF) using inverse dynamics models. Obtaining GRF from patients with CP is challenging. Typically developed (TD) individuals' joint moments were predicted from joint angles using machine learning, but no such study has been conducted on patients with CP. Accordingly, we aimed to predict the dorsi-plantar flexion, knee flexion-extension, hip flexion-extension, and hip adduction-abduction moments based on the trunk, pelvis, hip, knee, and ankle kinematics during gait in patients with CP and TD individuals using one-dimensional convolutional neural networks (CNN). The anonymized retrospective gait data of 329 TD (26 years ± 14, mass: 70 kg ± 15, height: 167 cm ± 89) and 917 CP (17 years ± 9, mass:47 kg ± 19, height:153 cm ± 36) individuals were evaluated and after applying inclusion-exclusion criteria, 132 TD and 622 CP patients with spastic diplegia were selected. We trained specific CNN models and evaluated their performance using isolated test subject groups based on normalized root mean square error (nRMSE) and Pearson correlation coefficient (PCC). Joint moments were predicted with nRMSE between 18.02% and 13.58% for the CP and between 12.55% and 8.58% for the TD groups, whereas with PCC between 0.85 and 0.93 for the CP and between 0.94 and 0.98 for the TD groups. Machine learning-based joint moment prediction from kinematics could replace conventional moment calculation in CP patients in the future, but the current level of prediction errors restricts its use for clinical decision-making today.