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

Matthew AhmadiMargaret O'NeilMaria Fragala-PinkhamNancy LennonStewart G Trost
Published 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.
Keyphrases
  • machine learning
  • cerebral palsy
  • deep learning
  • artificial intelligence
  • big data
  • blood pressure
  • mental health
  • healthcare
  • young adults
  • case report
  • label free
  • lower limb
  • quantum dots