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 (702) mHealth for Data Collection and Research (744) Fitness Trackers and Smart Pedometers/Accelerometers (506) Machine Learning (1121) Artificial Intelligence (822) Innovations and Technology for Physical Activity Education (593)

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'Driscoll 1 Author Orcid Image ;   Jake Turicchi 1 Author Orcid Image ;   Mark Hopkins 2 Author Orcid Image ;   Cristiana Duarte 1 Author Orcid Image ;   Graham W Horgan 3 Author Orcid Image ;   Graham Finlayson 1 Author Orcid Image ;   R James Stubbs 1 Author Orcid Image
Article Authors Cited by (3) 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.