Researchers and startups have identified smart textiles, a sub-field of the booming wearables industry, as a technology that may move the needle in several settings—sports, healthcare, factories, and beyond. Nevertheless, smart textiles still face two prevailing challenges: reliability and manufacturability.
In smart textiles, resistive pressure sensing works by leveraging a piezo-resistive element created by a conducting yarn. The sensor structure is often a 2D matrix of standard and conductive yarns, spun in a way that sandwiches a piezo-resistive knit around two layers of conductive yarn. A key feature of the piezo-resistive knit is that it changes its resistance based on the applied force, allowing pressure sensing within the fabric.
The team presented a manufacturing method dubbed “thermoforming.” Thermoplastic yarns are melted at relatively low temperatures to form a pliable textile using this method. The material not only becomes a better fit for the user but also hardens slightly, preventing unwanted rubbing and interaction between fibers. To put thermoforming in action, the researchers created a tubular knit textile with a digital circular knitting machine combining polyester, spandex, conductive, and TPU yarns in the knitting process. The tube was then cut into the shape of a specific body part and finally melted at low temperatures to conform better to the wearer. This final melting process yielded a more accurate sensor (because of the closer fit) while also removing the deleterious impact of noise in the fibers.
By treating the data as a heat map, the researchers could interpret it as if it were an image, simplifying the machine-learning aspect of the project. The MIT team says this technique allowed them to develop a personalized convolutional neural network (CNN) that recognized real-time activity and posture based on a user’s interaction with the textile surface.
