Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy.

ABSTRACT:

Background

Cerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, yet accurate measures of PA are needed to evaluate the effectiveness of surgical and therapy-based interventions to increase PA. Accelerometer-based motion sensors have become the standard for objectively measuring PA in children and adolescents; however, current methods for estimating physical activity intensity in children with CP are associated with significant error and may dramatically underestimate HPA in children with more severe mobility limitations. Machine learning (ML) models that first classify the PA type and then predict PA i

SUBMITTER: Ahmadi M 

PROVIDER: S-EPMC6238270 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature