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

Development and Evaluation of Research Methods, Instruments and Tools (439) Machine Learning (1400) Fitness Trackers and Smart Pedometers/Accelerometers (543)

Published on in Vol 4, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16727, first published .
Calibrating Wrist-Worn Accelerometers for Physical Activity Assessment in Preschoolers: Machine Learning Approaches

Calibrating Wrist-Worn Accelerometers for Physical Activity Assessment in Preschoolers: Machine Learning Approaches

Calibrating Wrist-Worn Accelerometers for Physical Activity Assessment in Preschoolers: Machine Learning Approaches

Authors of this article:

Shiyu Li1 Author Orcid Image ;   Jeffrey T Howard2 Author Orcid Image ;   Erica T Sosa2 Author Orcid Image ;   Alberto Cordova3 Author Orcid Image ;   Deborah Parra-Medina4 Author Orcid Image ;   Zenong Yin2 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Ahmadi M, Trost S, Bergman P. Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods. PLOS ONE 2022;17(4):e0266970 View
  2. Gao Z, Liu W, McDonough D, Zeng N, Lee J. The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities. Journal of Clinical Medicine 2021;10(24):5951 View
  3. Lettink A, Altenburg T, Arts J, van Hees V, Chinapaw M. Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0–5 years old). International Journal of Behavioral Nutrition and Physical Activity 2022;19(1) View
  4. Clanchy K, Stanfield M, Smits E, Liimatainen J, Ritchie C. Calibration and validation of physical behaviour cut-points using wrist-worn ActiGraphs for children and adolescents: A systematic review. Journal of Science and Medicine in Sport 2024;27(2):92 View