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SeamFit: Towards Practical Smart Clothing for Automatic Exercise Logging

Published in Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)/UbiComp 2025

Tianhong Catherine Yu, Manru Mary Zhang*, Luis Miguel Malenab*, Chi-Jung Lee, Jacky Hao Jiang, Ruidong ZhangFrancois Guimbretiere, and Cheng Zhang

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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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