Digital electronics in fibres enable fabric-based machine-learning inference
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 03, 2021
- Source ID
- 10.1038/s41467-021-23628-5
Entities
People
- Anna Gitelson-kahn
- Brian Wang
- Gabriel Loke
- Ioannis Chatziveroglou
- Itamar Chinn
- John D. Joannopoulos
- Johnny Fung
- Pin-wen Chou
- Stephanie Fu
- Syamantak Payra
- Tural Khudiyev
- Wei Yan
- Yoel Fink
- Yorai Shaoul
Organizations
- Defense Threat Reduction Agency
- National Science Foundation
- United States Army Research Laboratory