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, says Irmandy Wicaksono,
a research assistant in the MIT Media Lab and lead author of a paper presenting 3DKnITS.
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.
The research will be presented at the IEEE Engineering in Medicine and Biology Society
Conference.
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, Wicaksono explains.
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 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 degrees Celsius, 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.
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.
That thermoforming also allows the researchers to create 3D forms, like a sock or shoe, that
actually fit the precise size and shape of the user.
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.
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.
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.
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 percent accuracy and could recognize seven yoga poses
with 98.7 percent accuracy.
They also used a circular knitting machine to create a form-fitted smart textile shoe
with 96 pressure sensing points spread across the entire 3D textile.
They used the shoe to measure pressure exerted on different parts of the foot when the wearer
kicked 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, Wicaksono says.
He and his colleagues are also exploring more creative applications.
In collaboration with a sound designer and a contemporary dancer, they 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.
This research was recently presented at the ACM Creativity and Cognition Conference.
Interdisciplinary collaboration can create some really unique applications.
Now that the researchers have demonstrated the success of their fabrication technique,
Wicaksono 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.
In the medical field, and in orthopedic sports medicine specifically, this technology provides
the ability to better detect and classify motion and to recognize force distribution
patterns in real-world (out of the laboratory) situations.
This is the type of thinking that will enhance injury prevention and detection techniques
and help evaluate and direct rehabilitation.