Researchers at Cornell University have developed a new type of smart clothing that can track a person’s posture and exercise routine but looks, wears – and washes – just like a regular shirt.

The new technology, called SeamFit, uses flexible conductive threads sewn into the neck, arm and side seams of a standard short-sleeved T-shirt.

The user does not need to manually log their workout, because an artificial intelligence pipeline detects movements, identifies the exercise and counts reps. Afterward, the user simply removes a circuit board at the back neckline, and tosses the sweaty shirt into the washing machine.

Most existing body-tracking clothing is tight and restrictive or embedded with chunky sensors, according to Catherine Yu, doctoral student and lead researcher on the project.

We were interested in how we can make clothing smart without making it bulky or unusable, and to push the practicality so that people can treat it the way they would usually treat their clothing.”

Catherine Yu, doctoral student and lead researcher on the project

Alternatively, athletes can choose fitness trackers, like smartwatches or rings, but these are extra devices that people may not want to wear while exercising and can’t track movement across the entire body.

The study, “SeamFit: Towards Practical Smart Clothing for Automatic Exercise Logging,” published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, and will be presented at the UbiComp/ISWC 2025 meeting in October.

To test the shirts’ performance, the team recruited volunteers, who did a series of 14 exercises – including lunges, sit-ups and biceps curls – while wearing SeamFit. Without any calibration or training for each user, SeamFit’s model classified the exercises with 93.4% accuracy and successfully counted reps, with counts that, on average, were off by less than one.

SeamFit works because when people exercise, the threads’ capacitance – their ability to store charge – changes as the threads move, deform and interact with the human body. The circuit board at the back neckline measures the capacitances and transmits them through a Bluetooth connection to a computer. A customized, lightweight signal-processing and machine-learning pipeline then deciphers the movements.

More broadly, this type of technology could assist with human-AI interaction, because by tracking human movements and activities, AI can better understand when to interact and when to wait – such as when someone is eating or sleeping.

“While this paper demonstrated the approach for a simple garment, we believe it can easily be adapted to a wide range of garments and could take advantage of the complex seam patterns of advanced sportswear,” said co-author François Guimbretière, professor of information science.