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New Fabric-based Sensing System Can Detect a Variety of Joint Movements

Tribexor, a fabric-based, triboelectric, joint-sensing system

Electrical and computer engineering (ECE) graduate students Ali Kiaghadi and Morgan Baima were part of a team of UMass Amherst scientists who developed Tribexor, a fabric-based, triboelectric, joint-sensing system that can be integrated with loose-fitting clothing to sense a variety of joint movements such as flexion, extension, and velocity of joint movement. The UMass researchers introduced Tribexor in a paper presented at SenSys 2018, the 16th ACM Conference on Embedded Networked Sensor Systems, held from November 4 to 7 in Shenzhen, China. The information about Tribexor was reported in an Inside UMass article: New Fabric-Based Sensor Overcomes Loose Clothing Obstacle.

In addition to the two ECE grad students, the team included senior computer science researcher Jeremy Gummeson and professors Trisha Andrew, chemistry, and Deepak Ganesan, computer science.

As the researchers pointed out, in the field of wearable devices one area expected to grow dramatically in the next decade is smart clothing such as, for example, shirts, pants, bandages, and caps fitted with instruments to perform health-monitoring functions.

But, as Ganesan explained in the Inside UMass article, a fundamental problem for smart garments is whether they can obtain useful signals from loosely worn clothing. Many sensors, like inertial sensors and electromyography, require a tight fit to reduce motion artifacts and obtain a meaningful signal. But tight clothing is uncomfortable to wear and not appropriate in many applications such as elder care and patient care.

For their new device, the UMass researchers used functionalized triboelectric fabric developed in Andrew’s materials chemistry lab. The fabric is comprised of layers that transfer surface charge from one layer to another and generate a voltage or current when compressed, tugged, or twisted due to joint motion. This translates movement into an electrical signal and extracts useful information from loosely worn smart textiles.

Ganesan said that “Normally, loose fitting clothing would be considered a problem because that means we have to deal with a significant amount of noise, which is already a problem for relatively tight-fitting devices like fitness bands.” But the Tribexor device turns this limitation into an advantage, he notes, because loose-fitting clothing can fold, compress, and twist more.

The authors reported that Tribexor has 95 percent accuracy for detecting elbow and knee flexion and extension movements and 85 percent accuracy for estimating angular velocity of the elbow and knee joints. It also accurately detects a variety of activities of daily living, thus allowing it to be used instead of a smartwatch to monitor activity. (November 2018)