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](https://asset.jmir.pub/assets/b62fdbfbd9e371a61303517c6fd299da.png 480w,https://asset.jmir.pub/assets/b62fdbfbd9e371a61303517c6fd299da.png 960w,https://asset.jmir.pub/assets/b62fdbfbd9e371a61303517c6fd299da.png 1920w,https://asset.jmir.pub/assets/b62fdbfbd9e371a61303517c6fd299da.png 2500w)
Journals
- 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
- 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
- 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
- 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