Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy.
Matthew AhmadiMargaret O'NeilMaria Fragala-PinkhamNancy LennonStewart G TrostPublished in: Journal of neuroengineering and rehabilitation (2018)
ML methods provided acceptable classification accuracy for detection of a range of activities commonly performed by ambulatory children with CP. The resultant models can help clinicians more effectively monitor bouts of brisk walking in the community. The results indicate that 2-step models that first classify PA type and then predict energy expenditure using activity specific regression equations are worthy of exploration in this patient group.