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

Theme Issue 2020: Using Artificial Intelligence and Machine Learning to Improve E-health and M- health (1) Wearable Devices and Sensors (903) mHealth for Data Collection and Research (981) Fitness Trackers and Smart Pedometers/Accelerometers (571) Machine Learning (1501) Artificial Intelligence (1421) Innovations and Technology for Physical Activity Education (756)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23938, first published .
Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Authors of this article:

Ruairi O'Driscoll1 Author Orcid Image ;   Jake Turicchi1 Author Orcid Image ;   Mark Hopkins2 Author Orcid Image ;   Cristiana Duarte1 Author Orcid Image ;   Graham W Horgan3 Author Orcid Image ;   Graham Finlayson1 Author Orcid Image ;   R James Stubbs1 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.