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. 2020 Jul 10;10:11450. doi: 10.1038/s41598-020-68225-6

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© The Author(s) 2020

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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Illustration of the wearable motion analysis based on biomechanical simulations and sensor data synthesis. (A) Illustration of the sensor-extended, personal full-body model while running, including 834 simulated sensors uniformly positioned at upper and lower arms and legs, as well as feet. (B) Feet models with an inertia-free shoe model used to place sensor models. The vertical calcaneus position in y-axis orientation served as stride reference. (C) Example of the acceleration time series data synthesised for each simulated sensor. (D) Zoomed view of the lower arm (64 sensors), and the upper leg (96 sensors). For visualisation of the sensor positions, the femur was rotated. (E) Normalised root-mean-square-error (nRMSE) of the marker stride duration, derived between simulated sensors and the calcaneus reference. Colour-coded nRMSE maps shown here for athlete ID5.