Digitally programmable AI fibre stores music and movie

June 04, 2021 // By Nick Flaherty
Digitally programmable AI fibre stores music and movie
Researchers at MIT in the US have created a programmable fibre that can store and analyse data after being sewn into material.

The researchers at MIT created the fibre from hundreds of square silicon chips placed into a preform that was then used to create a polymer fibre. By precisely controlling the polymer flow, the researchers were able to create a fibre with continuous electrical connection between the chips over a length of tens of meters.

The fibre itself is thin and flexible and can be passed through a needle, sewn into fabrics, and washed at least 10 times without breaking down.

This builds on work at MIT published in 2018 with LED diodes successfully extruded in a polymer fibhre for wearable lighting.

“This work presents the first realization of a fabric with the ability to store and process data digitally, adding a new information content dimension to textiles and allowing fabrics to be programmed literally,” said Yoel Fink, who is a professor in the departments of materials science and engineering and electrical engineering and computer science, a Research Laboratory of Electronics principal investigator, and the senior author on a paper in Nature.

The researchers were able to write, store, and read information on the fibre including a 767Kbit full-colour short movie file and a 0.48Mbyte music file. The files can be stored for two months without power.

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The team at MIT also worked with the textile department at the Rhode Island School of Design (RISD). Associate Professor Anna Gitelson-Kahn at RISD incorporated the digital fibres into a knitted garment sleeve as the first step to a digital garment.

A number of the chips in the fibre support a neural network of 1,650 connections. After sewing it around the armpit of a shirt, the researchers used the fibre to collect 270 minutes of surface body temperature data from a person wearing the shirt, and analyze how these data corresponded to different physical activities. Using machine learning algorithms, the fibre was able to determine with 96 percent accuracy what activity the person wearing it was engaged in.

www.mit.edu

 

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