New digital fiber can store files in our clothes and sense our physical activity

June 3, 2021

MIT researchers have created the first fabric fiber to have digital capabilities ready to collect, store and analyze data using a neural network. (Anna Gittelson/Photo by Roni Cnaani)

Researchers from the Massachusetts Institute of Technology have designed the world's first fiber with digital capabilities that can be incorporated into fabric. Capable of storing music, videos or other files in clothing, the thread can also collect physiological data from the wearer in order to analyze and predict their activity, potentially revolutionizing the personal health technology industry.

In a paper published Thursday in Nature Communications, the researchers explained that their electronic fiber strand is digital. That means it uses a binary communication protocol of 1s and 0s, similar to that of computers. This sets it apart from existing electronic fibers for fabrics, which are analog rather than digital. 

Notably, existing fibers can connect to only a single device per fiber. By contrast, a single connection at the end of the MIT team's digital fiber allows it to access multiple devices, and each fiber can execute multiple functions. 

Yoel Fink, senior author of the paper and a professor of materials science and engineering at MIT, told The Academic Times that his research has focused on realizing a new vision of functionality for fibers, which he and his co-authors described in their paper as "the basic building blocks of fabrics."

"Fibers still do what they've always done," Fink said. "So my research has been to try to see if we can bring the world of devices and the world of function [together] to define a new path for fibers and align them with high-tech devices."

Fink continued, "This particular chapter is a very important one, in that it is the first time a digital component is realized within a fiber. So once you have memory in a fiber, that suddenly means that there's an information content to the fiber. That also means that you could program it, and you could store programs or other valuable data."

Fink and his colleagues developed prototypes of the digital fiber, which is flexible and thin enough to be passed through a needle and sewn into clothing. The work to sew the new fiber into fabrics for this project was done in collaboration with the textile department at the Rhode Island School of Design. The project was carried out with funding from the U.S. Army Research Office's Institute for Soldier Nanotechnologies, the National Science Foundation and the Department of Defense's Defense Threat Reduction Agency.

"The basic idea has been to get digital, addressable chips into fibers, and that's what we did," Fink said. The process involves putting hundreds of square, silicon microchips into a larger rectangular object called a "preform." The researchers heated the preform, causing the chips to flow into a long, microscopic fiber that contains dozens of chips with a continuous electrical connection between them.

Study participants wore shirts with the fiber sewn into the fabric around the armpit area in contact with the wearer's skin. In combination with a neural network, the digital fiber could take the wearer's temperature and infer their physical activity, such as sitting or walking, with an accuracy of over 96%. In addition, the researchers were able to write, store and read information on the fiber. They successfully stored a short movie file and a music file on the fiber for two months without power. And the fibers can withstand at least 10 wash cycles without breaking down.

"This is scientific work, so we don't necessarily have well-developed applications, but we are trying to get people to think about two directions. One direction is abstract, which is just adding an information content and dimension to something that didn't have it before, which is fabrics," Fink said. "Does that change the way we're going to think or interact with the fabric now that we know this fabric could store [things like] a book, a piece of music or scripture?"

Fink said the second direction is the practicality or utility of the invention. We think of the surface of our bodies as valued real estate, and we may be able to make better use of that real estate. 

"There's a lot of information that your body is communicating that we actually don't have the means to listen to or intercept," Fink said. That inaccessible yet valuable data includes information about our health and physical activities. To intercept that, sensing functions can be integrated into fabrics, but there needs to be a reliable way to analyze and store that information gleaned from smart fabric. This led the co-authors to explore this possibility and develop their current research.

"I think the big application area that I see is health care — being able to move health care from being a checkup-based business to [using] continuous monitoring signals," Fink said.

Because this fabric can track sleep and activity patterns and collect physiological data, Fink said clothing of the future has the potential to diagnose respiratory diseases, such as chronic obstructive pulmonary disease, asthma and even COVID-19. Clothing's advantage lies in the large surface area that it covers. Commercial wearable devices such as smartwatches or even headbands cover less of us, limiting the physical attributes they can track and the ailments they can detect. 

"That's why we feel that fabrics are very natural in that respect, and we don't need to convince you to carry something else that you don't want to," Fink said, referring to smartwatches and other wearable devices.

"There's a whole world of wearables, but this is not wearables. This is getting technology into fabrics and [further] into fibers. And that, for us, is a very foundational thing to do," he added. 

The team plans to continue working to bring this technology to real-world applications, and Fink expects there will be some commercial derivatives resulting from the research soon. 

The study, "Digital electronics in fibres enable fabric-based machine-learning inference," published June 3 in Nature Communications, was authored by Gabriel Loke, Tural Khudiyev, Brian Wang, Stephanie Fu, Syamantak Payra, Yorai Shaoul, Johnny Fung, Ioannis Chatziveroglou, Itamar Chinn, Wei Yan, John Joannopoulos and Yoel Fink, Massachusetts Institute of Technology; Pin-Wen Chou, Harrisburg University of Science and Technology; and Anna Gitelson-Kahn, Rhode Island School of Design.

We use cookies to improve your experience on our site and to show you relevant advertising.