Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 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"

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;
  2. 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
  3. Catal C, Akbulut A. Automatic energy expenditure measurement for health science. Computer Methods and Programs in Biomedicine 2018;157:31
  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
  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