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

Development and Evaluation of Research Methods, Instruments and Tools (1348) Fitness Trackers and Smart Pedometers/Accelerometers (799) Machine Learning (3225)

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 .
Children playing on a colorful jungle gym outdoors

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 (9) 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
  5. Phillips S, Clevenger K, Bruijns B, Tucker P, Vanderloo L, Loh A, Naveed M, Bourke M. Effect of Accelerometer Cut-Points on Preschoolers’ Physical Activity and Sedentary Time: A Systematic Review and Meta-Analysis. Journal for the Measurement of Physical Behaviour 2024;7(1) View
  6. Liang Y, Wang C, Hsiao C. Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review. Journal of Medical Internet Research 2024;26:e59497 View
  7. Zi Y, van de Ven S, de Geus E, Chen P. Machine and Deep Learning for Detection of Moderate-to-Vigorous Physical Activity From Accelerometer Data: Systematic Scoping Review. Interactive Journal of Medical Research 2026;15:e76601 View
  8. Rico-González M, Holsbrekken E, Gómez-Carmona C, Ardigò L. Machine Learning Applications for In-School Physical Activity Data Using IMUs in Children and Adolescents: A Systematic Review for Health Promotion. Journal of Primary Care & Community Health 2026;17 View
  9. Xiong X, Lai J, Wang S, Li J, Pei X, Sun J, An Y, Wang Z. Associations between objectively measured physical activity intensities and executive function in preschool children: a cross-sectional study. Frontiers in Psychiatry 2026;17 View