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

Journal: Nature communications
Published Date: Jun 3, 2021

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 × 10 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.

Authors

  • Gabriel Loke
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Tural Khudiyev
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Brian Wang
    Department of Radiation Oncology, University of Louisville, Louisville, KY, USA.
  • Stephanie Fu
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Syamantak Payra
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yorai Shaoul
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Johnny Fung
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Ioannis Chatziveroglou
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Pin-Wen Chou
    Harrisburg University of Science and Technology, Harrisburg, PA, USA.
  • Itamar Chinn
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Wei Yan
    State & Local Joint Engineering Research Center of Green Pesticide Invention and Application, College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China. Electronic address: yanwei@njau.edu.cn.
  • Anna Gitelson-Kahn
    Textile Department, Rhode Island School of Design, Providence, RI, USA.
  • John Joannopoulos
    Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yoel Fink
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. yoel@mit.edu.

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