Using a novel fabrication process, MIT researchers have produced smart textiles that snugly conform to the body so they can sense the wearer’s posture and motions.
By incorporating a special type of plastic yarn and using heat to slightly melt it — a process called thermoforming — the researchers were able to greatly improve the precision of pressure sensors woven into multilayered knit textiles, which they call 3DKnITS.
They used this process to create a “smart” shoe and mat, and then built a hardware and software system to measure and interpret data from the pressure sensors in real time. The machine-learning system predicted motions and yoga poses performed by an individual standing on the smart textile mat with about 99 percent accuracy.
Their fabrication process, which takes advantage of digital knitting technology, enables rapid prototyping and can be easily scaled up for large-scale manufacturing, said Irmandy Wicaksono, a research assistant in the MIT Media Lab and lead author of a paper presenting 3DKnITS.
To produce a smart textile, the researchers use a digital knitting machine that weaves together layers of fabric with rows of standard and functional yarn. The multilayer knit textile is composed of two layers of conductive yarn knit sandwiched around a piezoresistive knit, which changes its resistance when squeezed. Following a pattern, the machine stitches this functional yarn throughout the textile in horizontal and vertical rows. Where the functional fibers intersect, they create a pressure sensor, said Wicaksono.
But yarn is soft and pliable, so the layers shift and rub against each other when the wearer moves. This generates noise and causes variability that make the pressure sensors much less accurate.
Wicaksono came up with a solution to this problem while working in a knitting factory in Shenzhen, China, where he spent a month learning to program and maintain digital knitting machines. He watched workers making sneakers using thermoplastic yarns that would start to melt when heated above 70 °C, which slightly hardens the textile, so it can hold a precise shape. He decided to try incorporating melting fibers and thermoforming into the smart textile fabrication process.
Once he perfected the fabrication process, Wicaksono needed a system to accurately process pressure sensor data. Since the fabric is knit as a grid, he crafted a wireless circuit that scans through rows and columns on the textile and measures the resistance at each point.
Inspired by deep-learning techniques for image classification, Wicaksono devised a system that displays pressure sensor data as a heat map. Those images are fed to a machine-learning model, which is trained to detect the posture, pose, or motion of the user based on the heat map image.
The high accuracy of 3DKnITS could make them useful for applications in prosthetics, where precision is essential. A smart textile liner could measure the pressure a prosthetic limb places on the socket, enabling a prosthetist to easily see how well the device fits.
The technique could have many applications, especially in health care and rehabilitation. For example, it could be used to produce smart shoes that track the gait of someone who is learning to walk again after an injury, or socks that monitor pressure on a diabetic patient’s foot to prevent the formation of ulcers.
For more information, contact Abby Abazorius at