Digital electronics in fibres enable fabric-based machine-learning inference

Gabriel Loke, Tural Khudiyev, Brian Wang, Stephanie Fu, Syamantak Payra, Yorai Shaoul, Johnny Fung, Ioannis Chatziveroglou, Pin-Wen Chou, Itamar Chinn, Wei Yan, Anna Gitelson-Kahn, John Joannopoulos and Yoel Fink ()
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Gabriel Loke: Massachusetts Institute of Technology
Tural Khudiyev: Massachusetts Institute of Technology
Brian Wang: Massachusetts Institute of Technology
Stephanie Fu: Massachusetts Institute of Technology
Syamantak Payra: Massachusetts Institute of Technology
Yorai Shaoul: Massachusetts Institute of Technology
Johnny Fung: Massachusetts Institute of Technology
Ioannis Chatziveroglou: Massachusetts Institute of Technology
Pin-Wen Chou: Harrisburg University of Science and Technology
Itamar Chinn: Massachusetts Institute of Technology
Wei Yan: Massachusetts Institute of Technology
Anna Gitelson-Kahn: Rhode Island School of Design
John Joannopoulos: Massachusetts Institute of Technology
Yoel Fink: Massachusetts Institute of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 105 bits per metre. The entire ensemble of devices are individually addressable and independently operated through a single connection at the fibre edge, overcoming the perennial single-fibre single-device limitation and increasing system reliability. The digital fibre, when incorporated within a shirt, collects and stores body temperature data over multiple days, and enables real-time inference of wearer activity with an accuracy of 96% through a trained neural network with 1650 neuronal connections stored within the fibre. The ability to realise digital devices within a fibre strand which can not only measure and store physiological parameters, but also harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context.

Date: 2021
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DOI: 10.1038/s41467-021-23628-5

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