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Smart Textiles

Highly accurate data with 3DKnITS

Fabrication process enables rapid prototyping and can be easily scaled up for large-scale manufacturing

11th July 2022

Innovation in Textiles
 |  Cambridge, MA, USA

Medical/Hygiene, Sports/​Outdoor

Researchers at MIT have greatly improved the precision of pressure sensors woven into multi-layered knitted textiles, which they call 3DKnITS, via a new thermoforming process.

They have used the process to create a smart shoe and mat and 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 mat with 99% accuracy.

The fabrication process, which exploits digital knitting technology, enables rapid prototyping and can be easily scaled up for large-scale manufacturing, according to Irmandy Wicaksono, a research assistant at the MIT Media Lab.

“The technique could have many applications, especially in health care and rehabilitation,” he said. “It could, for example, 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 feet to prevent the formation of ulcers.

“With digital knitting, you have this freedom to design your own patterns and also integrate sensors within the structure itself, so it becomes seamless and comfortable, and you can develop it based on the shape of the body.”

“Some of the early pioneering work on smart fabrics happened here in the late 90s,” added Professor Joseph A. Paradiso of the Media Lab. “The materials, embeddable electronics and fabrication machines have advanced enormously since then. It’s a great time to see our research returning to this area, with sensing and functions diffusing more fluidly into materials and opening up enormous possibilities.”

Digital knitting

The multilayer knitted textile is composed of two layers of conductive yarn sandwiched around a piezoresistive layer, 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 fibres intersect, they create a pressure sensor.

The multilayer knitted textiles are composed of two layers of conductive knitted yarns sandwiched around a piezoresistive later, which changes its resistance when squeezed.

Yarns, however, are 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 programme and maintain digital knitting machines. He watched workers making sports shoes 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 fibres and thermoforming into the smart textile fabrication process.

“The thermoforming really solves the noise issue because it hardens the multilayer textile into one layer by essentially squeezing and melting the whole fabric together, which improves the accuracy,” he said. “That thermoforming also allows us to create 3D forms, like a sock or shoe, that actually fit the precise size and shape of the user,” he says.

Once he perfected the fabrication process, Wicaksono needed a system to accurately process pressure sensor data. Since the fabric is knitted as a grid, he crafted a wireless circuit that scans through rows and columns on the textile and measures the resistance at each point. He designed this circuit to overcome artifacts caused by “ghosting” –ambiguities which occur when the user exerts pressure on two or more separate points simultaneously.

Inspired by deep-learning techniques for image classification, Wicaksono devised a system that displays pressure sensor data as a heat map. The 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.

Analysing activities

Once the model was trained, it could classify the user’s activity on the smart mat (walking, running, doing push-ups, etc.) with 99.6% accuracy and could recognise seven yoga poses with 98.7% accuracy.

A circular knitting machine was also used to create a form-fitted smart textile shoe with 96 pressure sensing points spread across the entire 3D textile. It has been used to measure pressure exerted on different parts of the foot when a wearer kicks a soccer ball.  

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 researchers are also exploring more creative applications. In collaboration with a sound designer and a contemporary dancer, they have developed a smart textile carpet that drives musical notes and soundscapes based on the dancer’s steps, to explore the bidirectional relationship between music and choreography.

Wicaksono now plans to refine the circuit and machine learning model. Currently, the model must be calibrated to each individual before it can classify actions, which is a time-consuming process. Removing that calibration step would make 3DKnITS easier to use. The researchers also want to conduct tests on smart shoes outside the lab to see how environmental conditions like temperature and humidity impact the accuracy of sensors.

www.mit.edu

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