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.
Highlights
Digital devices are the essential building blocks of any modern electronic system
Hundreds of individually addressable digital devices are electrically connected in situ during the fibre drawing process, with all devices accessible on the same in-fibre digital communication bus
A single connection at the end of the fibre is used to access multiple devices independently, enabling the operation of multiple functions from a single fibre, overcoming the single-fibre singledevice limitation faced by prior approaches
Summary
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. The ability to realise digital devices within a fibre strand which can measure and store physiological parameters, and harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context. Without digressing much into semantics, it is noteworthy that the term wearable itself does not apply to most of the products we wear which are referred to as clothes These are typically made of fabrics and have the a priori advantage of being in physical contact with large surface areas of the human body and already are a fact of life for all segments of society. We aim to store in the fibre sensory data but a neural network trained to infer context from it
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