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. 2021 Jun 3;12:3317. doi: 10.1038/s41467-021-23628-5

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© The Author(s) 2021

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. 5 — a Schematic of the shirt integrated with a digital fibre that has sensors, data storage, customisable programmes, and a neural network stored within its memory devices. b This digital fabric is capable of running a neural network that recognises what the wearer is doing in real-time without any human intervention. The operation of the neural network is as follows: a 12-s temperature measurement is measured in real-time and inputted into the in-fibre convolutional neural network (CNN) which is comprised of feature extraction, two convolutional layers, and four neuronal layers. After these layers, the fibre gives a final decision on the type of activity performed by the wearer. Upon decision-making, custom activity- and temperature-dependent programmes, stored in the fibre, provide feedback on the wearer’s physiological status (Supplementary Fig. 11). c The accuracy of human activity recognition by the in-fabric CNN is shown by the prediction table, indicating an average accuracy of 96.4%.