‘SeamFit’ by researchers at Cornell University is a new type of unobtrusive smart clothing with detachable conductive threads on top of the seams that can track a person’s posture and exercise routine but looks, wears – and washes – just like a regular shirt.
The research team led by Catherine Yu, a doctoral student in the field of information science exploited the seams, found in most mass-produced clothing, to make the comfortable, affordable piece of smart clothing.
- The new technology 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.
- The team envisions that SeamFit could be useful for athletes, fitness enthusiasts and patients engaged in physical therapy. This type of unobtrusive smart clothing could be especially useful for athletes logging their exercise routines and for physical therapists monitoring the progress of patients at home.
- 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.
THE STUDY— SeamFit: Towards Practical Smart Clothing for Automatic Exercise Logging— was published in March in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
THE TECH: Three SeamFit shirts were constructed – in small, medium and large – using a home sewing machine to attach conductive threads on top of the seams. The three sizes allowed users to choose a looser or tighter fit, but did complicate the process of interpreting each user’s movements.
- To test the shirts’ performance, the team recruited 15 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 customised, lightweight signal-processing and machine-learning pipeline then deciphers the movements.
- After the workouts, the shirts were washed and dried at home.
- The project is a new iteration of SeamPose, a previous effort to track body postures using conductive threads in eight seams of a long-sleeve T-shirt.
LOOKING AHEAD: The team is currently exploring how the manufacturing process could be affordably scaled up, using industrial serger machines – which sew and make seams using three or four threads simultaneously – and more robust conductive threads.
TEAM & FUNDING: Additional authors on the study include Manru Mary Zhang ’25 and Luis Miguel Malenab ’25; Chi-Jung Lee and Ruidong Zhang, both doctoral students in the field of information science; and Jacky Hao Jiang, a visiting undergraduate from Rice University.
- Funding for this work came from the National Science Foundation.
WHAT THEY SAID:
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.
— François Guimbretière
Professor, Information Science
Cornell University
By just replacing a single thread in this mass manufacturing process, all of the clothing could easily become smart and be able to have this motion tracking capability. I’m imagining one day, you open your closet and there’s really no difference between smart and nonsmart clothing.
— Catherine Yu (Lead Researcher)
Doctoral Student
Cornell University
