Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to imp...

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Bibliografski detalji
Glavni autori: Matthew N. Ahmadi, Margaret E. O’Neil, Emmah Baque, Roslyn N. Boyd, Stewart G. Trost
Format: Članak
Jezik:English
Izdano: MDPI AG 2020-07-01
Serija:Sensors
Teme:
accelerometers
wearable sensors
exercise
measurement
GMFCS level
Online pristup:https://www.mdpi.com/1424-8220/20/14/3976

Internet

https://www.mdpi.com/1424-8220/20/14/3976

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