Back brace treatment is effective for treating scoliosis, but can be uncomfortable and time-consuming, involving much trial and error to arrive at the best fit. The team, led by Glasgow University, combined a 3D printed lattice structure with piezoresistance to create a self-sensing back brace that can inform medical professionals how it is being strained by individual scoliosis patients.
The brace combines polypropylene with carbon nanotubes to create a cellular material capable of sensing the amount of stress it experiences when worn. Using 3D printing, the team created lattice-like structures made from this material and subjected them to static and cyclic loadings, while measuring how the change in electrical resistance of the smart composite under load. This measure of the material’s changing piezoresistance is what could help to create a smart brace by allowing medics to see which areas of the body are exerting the most pressure on the brace.
Tests showed that after 100 cycles of loading and unloading the material retained its ability to sense the strain experienced by the material, suggesting it could be sufficiently smart to make it suitable for use in a back brace. The work is published in ACS Applied Materials and Interfaces.
“Scoliosis is a painful and debilitating disorder, and while the current generation of braces are better than they have ever been, there is still a lot of room for improvement,” said corresponding author Dr Shanmugam Kumar, from Glasgow University’s James Watt School of Engineering. “The self-sensing material that we’ve developed has a great deal of potential to deliver that next generation of improvement. What we hope to see is a future where scoliosis patients can be individually assessed by a doctor and have a 3D-printed brace produced for them which is unique to their condition.
“Then, after a few weeks of wearing it, they can return to their doctor and use the readout from the piezoresistive strain sensing brace to have it adjusted to make it even more effective, without the trial-and-error process that clinicians have to rely on at the moment.