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. 2023 Nov 15;6:207. doi: 10.1038/s41746-023-00926-4

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Overview of the method for modeling heart rate response to exercise with wearable data. The top panel describes the study population data and details of the wearable workout measurements used. The two bottom panels describe the training procedure and how the model components are trained to learn the personalized representation and ODE parameters using only heart rate and workout data. Learned representations can then be used downstream to predict various physiological traits, e.g., Fitness, BMI, and age.