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

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dc.contributor.authorArslan, Yunus Ziya
dc.contributor.authorÖzateş, Mustafa Erkam
dc.contributor.authorKarabulut, Derya
dc.contributor.authorWolf, Sebastian Immanuel
dc.contributor.authorSalami, Firooz
dc.date.accessioned2024-04-04T13:37:58Z
dc.date.available2024-04-04T13:37:58Z
dc.date.issued2023
dc.departmentTAÜ, Fen Bilimleri Enstitüsü, Robotlar ve Akıllı Sistemler Ana Bilim Dalı Koleksiyonuen_US
dc.description.abstractJoint 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.
dc.identifier.citationArslan, Yunus Z., Özateş, Mustafa E., Karabulut, D., Wolf, Sebastian I., Salami, F. (2023). Machine learning-based prediction of joint moments based on kinematics inpatients with cerebral palsy. Journal of Biomechanics içerisinde 155.
dc.identifier.doi10.1016/j.jbiomech.2023.111668
dc.identifier.scopus2-s2.0-85163375644
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0021929023002373?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1000
dc.identifier.volume155en_US
dc.identifier.wosWOS:001017232700001
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherJournal of Biomechanics
dc.relation.ispartofJournal of Biomechanics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectConvolutional neural networksen_US
dc.subjectJoint momentsen_US
dc.subjectGait kinematicsen_US
dc.subjectComputational gait analysisen_US
dc.subjectCerebral palsyen_US
dc.subjectMachine Learningen_US
dc.titleMachine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy
dc.typeArticle

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