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Abstract Electronic textiles capable of sensing, powering, and communication can be used to non-intrusively monitor human health during daily life. However, achieving these functionalities with clothing is challenging because of limitations in the electronic performance, flexibility and robustness of the underlying materials, which must endure repeated mechanical, thermal and chemical stresses during daily use. Here, we demonstrate electronic textile systems with functionalities in near-field powering and communication created by digital embroidery of liquid metal fibers. Owing to the unique electrical and mechanical properties of the liquid metal fibers, these electronic textiles can conform to body surfaces and establish robust wireless connectivity with nearby wearable or implantable devices, even during strenuous exercise. By transferring optimized electromagnetic patterns onto clothing in this way, we demonstrate a washable electronic shirt that can be wirelessly powered by a smartphone and continuously monitor axillary temperature without interfering with daily activities.
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This content will become publicly available on March 3, 2026
SeamFit: Towards Practical Smart Clothing for Automatic Exercise Logging
Smart clothing has exhibited impressive body pose/movement tracking capabilities while preserving the soft, comfortable, and familiar nature of clothing. For practical everyday use, smart clothing should (1) be available in a range of sizes to accommodate different fit preferences, and (2) be washable to allow repeated use. In SeamFit, we demonstrate washable T-shirts, embedded with capacitive seam electrodes, available in three different sizes, for exercise logging. Our T-shirt design, customized signal processing & machine learning pipeline allow the SeamFit system to generalize across users, fits, and wash cycles. Prior wearable exercise logging solutions, which often attach a miniaturized sensor to a body location, struggle to track exercises that mainly involve other body parts. SeamFit T-shirt naturally covers a large area of the body and still tracks exercises that mainly involve uncovered joints (e.g., elbows and the lower body). In a user study with 15 participants performing 14 exercises, SeamFit detects exercises with an accuracy of 89%, classifies exercises with an accuracy of 93.4%, and counts exercises with an error of 0.9 counts, on average. SeamFit is a step towards practical smart clothing for everyday uses.
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- Award ID(s):
- 2239569
- PAR ID:
- 10584083
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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