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

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 similar to 7.6 x 10(5) 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.

Automated summary

Digital devices are essential for modern electronic systems, and fibres containing digital devices have various applications such as physiological monitoring and human-computer interfaces. A scalable preform-to-fibre approach can produce flexible fibres with high storage density, containing hundreds of digital temperature sensors and memory devices. These digital fibres can collect and store body temperature data, and infer wearer activity in real-time with high accuracy through a trained neural network.