Conductive threads on skin track body movement

While there already are body movement-tracking systems, many of them incorporate cumbersome wearable devices, or require the person to move about in front of an array of cameras. A new technology, however, gets the job done simply using threads placed flat against the skin.

Developed at Massachusetts’ Tufts University, the experimental system incorporates threads that are coated in an electrically conductive carbon-based ink. As those threads bend in response to externally applied mechanical strain, their conductivity changes. Therefore, by monitoring those changes, it’s possible to ascertain when and to what extend the threads are being bent.

In a test of the technology, the research team placed two of the threads on the back of a test subject’s neck. Those threads were arranged in opposite orientations, crossing over one another so they formed a broad X. This configuration placed them along two different axes.

An electrical current was then run through the threads, as the test subject moved their head (which also involved moving their neck). The resulting changes in thread impedance were transmitted by Bluetooth to a computer, where machine learning-based algorithms were used to match those changes to specific head movements in real time.

All told, the system was 93 percent accurate at identifying variations in the direction, angle of rotation, and degree of displacement of the head. The technology should likely work just as well at tracking the movement of other body parts, although the algorithms would have to be specially trained for each one.

It is hoped that once developed into the form of thin skin patches or even form-fitting clothing, the system could be put to uses such as discretely monitoring athletic performance, checking that truck drivers aren’t getting drowsy, or monitoring patients with Parkinson’s disease.

“This is a promising demonstration of how we could make sensors that monitor our health, performance, and environment in a non-intrusive way,” says undergraduate student Yiwen Jiang, first author of a paper on the study. “More work needs to be done to improve the sensors’ scope and precision, which in this case could mean gathering data from a larger array of threads regularly spaced or arranged in a pattern, and developing algorithms that improve the quantification of articulated movement.”

The paper was published this week in the journal Scientific Reports.

Source: Tufts University via EurekAlert

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