A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility...

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Autori principali: Kevin D. McCay, Pengpeng Hu, Hubert P. H. Shum, Wai Lok Woo, Claire Marcroft, Nicholas D. Embleton, Adrian Munteanu, Edmond S. L. Ho
Natura: Articolo
Lingua:English
Pubblicazione: IEEE 2022-01-01
Serie:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Soggetti:
Cerebral palsy
early diagnosis
explainable AI
general movements assessment
machine learning
motion analysis
Accesso online:https://ieeexplore.ieee.org/document/9662301/

Accesso online

https://ieeexplore.ieee.org/document/9662301/

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