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

Autor: Yoel Fink, Yorai Shaoul, Pin-Wen Chou, Ioannis Chatziveroglou, Itamar Chinn, Wei Yan, Brian Wang, Anna Gitelson-Kahn, Stephanie Fu, Gabriel Loke, Tural Khudiyev, Johnny Fung, John D. Joannopoulos, Syamantak Payra
Rok vydání: 2021
Předmět:
Computer science
Science
Reliability (computer networking)
General Physics and Astronomy
Inference
ComputerApplications_COMPUTERSINOTHERSYSTEMS
02 engineering and technology
010402 general chemistry
01 natural sciences
Article
General Biochemistry
Genetics and Molecular Biology
Body Temperature
Machine Learning
User-Computer Interface
Wearable Electronic Devices
Memory
Electronic devices
Humans
Electronics
Electronic systems
Monitoring
Physiologic
Digital electronics
Digital Technology
Multidisciplinary
Artificial neural network
business.industry
Textiles
fungi
food and beverages
General Chemistry
021001 nanoscience & nanotechnology
Electrical and electronic engineering
0104 chemical sciences
Design
synthesis and processing
Remote Sensing Technology
Scalability
Neural Networks
Computer
Enhanced Data Rates for GSM Evolution
0210 nano-technology
business
Computer hardware
Zdroj: Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Nature Communications
ISSN: 2041-1723
DOI: 10.1038/s41467-021-23628-5
Popis: 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.
Implementation of digital electronics into fibres can enable real time monitoring of human physiological functions. Loke et al. show how digital functionalities can be incorporated into thin flexible polymeric fibre strands and applied for on-body machine-learning and intelligent textiles.
Databáze: OpenAIRE
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