Machine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Journal of Biomechanics
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Convolutional neural networks, Joint moments, Gait kinematics, Computational gait analysis, Cerebral palsy, Machine Learning
Kaynak
Journal of Biomechanics
WoS Q Değeri
Scopus Q Değeri
Cilt
155
Sayı
Künye
Arslan, 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.
