Programmable fiber created to sense, store personal data, Headlines Online

Programmable fiber senses, store data

MIT researchers have created the first fiber with digital capabilities, able to sense, store, analyze and infer activity after being sewn into a shirt.

Yoel Fink, a professor in the departments of materials science and engineering and electrical engineering and computer science, said digital fibers expand the possibilities for fabrics to uncover the context of hidden patterns in the human body that could be used for physical performance monitoring, medical inference, and early disease detection.

Until now, electronic fibers have been analog – carrying a continuous electrical signal – rather than digital, where discrete bits of information can be encoded and processed in 0s and 1s.

“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,” Fink said.

Memory and more

The fiber was created by placing hundreds of square silicon microscale digital chips into a preform that was then used to create a polymer fiber. By precisely controlling the polymer flow, the researchers were

A close-up photograph of the digital fibers on green fabric. Courtesy: Anna Gittelson and Roni Cnaani, Massachusetts Institute of Technology

able to create a fiber with continuous electrical connection between the chips over a length of tens of meters.

The fiber itself is thin and flexible and can be passed through a needle, sewn into fabrics, and washed at least 10 times without breaking down. Loke said, “When you put it into a shirt, you can’t feel it at all. You wouldn’t know it was there.” Making a digital fiber, he said, “Opens up different areas of opportunities and actually solves some of the problems of functional fibers.”

It offers a way to control individual elements within a fiber, from one point at the fiber’s end. “You can think of our fiber as a

Solid-state batteries add performance

SOLID-STATE batteries pack a lot of energy into a small space, but electrodes are not good at keeping in touch with electrolytes. Liquid electrolytes reach all areas of an electrode to spark energy, but liquids take space, don’t store energy and fail in time. Researchers are using solid electrolytes made of strategically arranged materials. A study led by University of Illinois UrbanaChampaign engineers demonstrates how control over the atomic alignment of solid materials can improve the cathode-solid electrolyte interface and stability in solid-state batteries. The team built electrodes containing sodium and lithium ions with specific atomic arrangements. They found correlations between battery performance and interface atomic arrangement in the lithium- and sodium-based solid-state batteries. They said minimizing the interface surface area and controlling the electrodes’ atomic alignment is key to understanding the nature of interface instabilities and improving cell performance. The controls helped simulations for future designs. ce Lois Yoksoulian, physical sciences editor, University of Illinois at Urbana-Champaign. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com. corridor, and the elements are like rooms, and they each have their own unique digital room numbers,” Loke said. The research team devised a digital addressing method that allows them to “switch on” the functionality of one element without turning on all the elements.

Fink noted the research at MIT was in close collaboration with the textile department at RISD led by Missakian. GitelsonKahn incorporated the digital fibers into a knitted garment sleeve, thus paving the way to creating the first digital garment.

On-body artificial intelligence

The fiber also takes a few steps forward into artificial intelligence by including, within the fiber memory, a neural network of 1,650 connections. After sewing it around the armpit of a shirt, the researchers used the fiber 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. Trained on these data, the fiber was able to determine with 96% accuracy what activity the person wearing it was engaged in.

Adding an AI component to the fiber increases its possibilities, the researchers said. Fabrics with digital components can collect a lot of information across the body over time, and these “lush data” are perfect for machine learning algorithms, Loke said. “This type of fabric could give quantity and quality open-source data for extracting out new body patterns that we did not know about before.”

With analytic power, the fibers someday could sense and alert people in real-time to health changes like a respiratory decline or an irregular heartbeat, or deliver muscle activation or heart rate data to athletes in training. The fiber is controlled by a small external device, so the next step will be to design a new chip as a microcontroller that can be connected within the fiber.

“When we can do that, we can call it a fiber computer,” Loke said. ce

Becky Ham,Massachusetts Institute of Technology. Edited by Chris Vavra, web content manager, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.

Controlling how AI systems create images

RESEARCHERS from North Carolina State University have developed a method for controlling how artificial intelligence (AI) systems create images. The work has applications for autonomous robotics and AI training.

In a type of AI task called conditional image generation, AI systems create images that meet a set of conditions. A system could be trained to create original images of cats or dogs, depending on the animal requested. More recent techniques have built on this to incorporate conditions regarding an image layout. This allows users to specify which types of objects they want to appear in particular places on the screen. The sky might go in one box, a tree might be in another box, a stream might be in a separate box, and so on. The new work builds on those techniques to give users more control over the resulting images, and to retain characteristics across a series of images.

“Our approach is highly reconfigurable,” said Tianfu Wu, co-author of the related paper and an assistant professor of computer engineering at NC State. “Ours also allows you to retain an image and add to it .... users could have the AI create a mountain scene ... then have the system add skiers to that scene.” The new approach uses AI to manipulate elements so they are identifiably the same, but have moved or changed in some way. ce Matt Shipman, research communications lead, North Carolina State University. Edited by Chris Vavra, web content manager, Control Engineering.

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