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Estimating ground reaction forces from gait kinematics in cerebral palsy: a convolutional neural network approach

While gait analysis is essential for assessing neuromotor disorders like cerebral palsy (CP), capturing accurate ground reaction force (GRF) measurements during natural walking presents challenges, particularly due to variations in gait patterns. Previous studies have explored GRF prediction using m...

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Hauptverfasser: Özateş, Mustafa Erkam (VerfasserIn) , Salami, Firooz (VerfasserIn) , Wolf, Sebastian Immanuel (VerfasserIn) , Arslan, Yunus Ziya (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: March 2025
In: Annals of biomedical engineering
Year: 2025, Jahrgang: 53, Heft: 3, Pages: 634-643
ISSN:1573-9686
DOI:10.1007/s10439-024-03658-y
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s10439-024-03658-y
Verlag, lizenzpflichtig, Volltext: https://link.springer.com/article/10.1007/s10439-024-03658-y
Volltext
Verfasserangaben:Mustafa Erkam Ozates, Firooz Salami, Sebastian Immanuel Wolf, Yunus Ziya Arslan
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Zusammenfassung:While gait analysis is essential for assessing neuromotor disorders like cerebral palsy (CP), capturing accurate ground reaction force (GRF) measurements during natural walking presents challenges, particularly due to variations in gait patterns. Previous studies have explored GRF prediction using machine learning, but specific focus on patients with CP is lacking. This research aims to address this gap by predicting GRF using joint angles derived from marker data during gait in patients with CP, thereby suggesting a protocol for gait analysis without the need for force plates.
Beschreibung:Gesehen am 16.06.2025
Beschreibung:Online Resource
ISSN:1573-9686
DOI:10.1007/s10439-024-03658-y

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