Explore our sister journals here.

Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 19.07.16 in Vol 3, No 2 (2016): Jul-Dec

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

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

Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han

JMIR Rehabil Assist Technol 2016 (Jul 19); 3(2):e7 

According to Crossref, the following articles are citing this article (DOI 10.2196/rehab.4340):

(note that this is only a small subset of citations)

  1. Killian M, Buchowski MS, Donnelly T, Burnette WB, Markham LW, Slaughter JC, Xu M, Crum K, Damon BM, Soslow JH. Beyond ambulation: Measuring physical activity in youth with Duchenne muscular dystrophy. Neuromuscular Disorders 2020;30(4):277
    CrossRef
  2. Catal C, Akbulut A. Automatic energy expenditure measurement for health science. Computer Methods and Programs in Biomedicine 2018;157:31
    CrossRef
  3. Tambuyzer E, Vandendriessche B, Austin CP, Brooks PJ, Larsson K, Miller Needleman KI, Valentine J, Davies K, Groft SC, Preti R, Oprea TI, Prunotto M. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nature Reviews Drug Discovery 2020;19(2):93
    CrossRef
  4. Pires IM, Felizardo V, Pombo N, Drobics M, Garcia NM, 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
    CrossRef
  5. Fergus P, Hussain AJ, Hearty J, Fairclough S, Boddy L, Mackintosh K, Stratton G, Ridgers N, Al-Jumeily D, Aljaaf AJ, Lunn J. A machine learning approach to measure and monitor physical activity in children. Neurocomputing 2017;228:220
    CrossRef
  6. Liu Y, Qu H, Wenocur AS, 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
    CrossRef
  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
    CrossRef