This paper is in the following e-collection/theme issue:

Emerging Technologies for Rehabilitation (241) Portable and Mobile Technologies for Rehabilitation (142) Fitness Trackers and Smart Pedometers/Accelerometers (541)

Published on in Vol 3, No 2 (2016): Jul-Dec

This is a member publication of UC Davis - Shields Library, Davis, CA, USA

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Authors of this article:

Amit Pande1 Author Orcid Image ;   Prasant Mohapatra1 Author Orcid Image ;   Alina Nicorici2 Author Orcid Image ;   Jay J Han2 Author Orcid Image
Article Authors Cited by (7) Tweetations (28) Metrics

Journals

  1. Killian M, Buchowski M, Donnelly T, Burnette W, Markham L, Slaughter J, Xu M, Crum K, Damon B, Soslow J. Beyond ambulation: Measuring physical activity in youth with Duchenne muscular dystrophy. Neuromuscular Disorders 2020;30(4):277 View
  2. Catal C, Akbulut A. Automatic energy expenditure measurement for health science. Computer Methods and Programs in Biomedicine 2018;157:31 View
  3. Tambuyzer E, Vandendriessche B, Austin C, Brooks P, Larsson K, Miller Needleman K, Valentine J, Davies K, Groft S, Preti R, Oprea T, Prunotto M. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nature Reviews Drug Discovery 2020;19(2):93 View
  4. Pires I, Felizardo V, Pombo N, Drobics M, Garcia N, Flórez-Revuelta F. Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer. Journal of Ambient Intelligence and Smart Environments 2018;10(4):315 View
  5. Fergus P, Hussain A, Hearty J, Fairclough S, Boddy L, Mackintosh K, Stratton G, Ridgers N, Al-Jumeily D, Aljaaf A, Lunn J. A machine learning approach to measure and monitor physical activity in children. Neurocomputing 2017;228:220 View
  6. Liu Y, Qu H, Wenocur A, Qu J, Chang X, Glessner J, Sleiman P, Tian L, Hakonarson H. Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development. JMIR Biomedical Engineering 2020;5(1):e20506 View
  7. Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023;11(3):887 View