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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. 

We present Ring-a-Pose, a single untethered ring that tracks continuous 3D hand poses. Located in the center of the hand, the ring emits an inaudible acoustic signal that each hand pose reflects differently. Ring-a-Pose imposes minimal obtrusions on the hand, unlike multi-ring or glove systems. It is not affected by the choice of clothing that may cover wrist-worn systems. In a series of three user studies with a total of 36 participants, we evaluate Ring-a-Pose's performance on pose tracking and micro-finger gesture recognition. Without collecting any training data from a user, Ring-a-Pose tracks continuous hand poses with a joint error of 14.1mm. The joint error decreases to 10.3mm for fine-tuned user-dependent models. Ring-a-Pose recognizes 7-class micro-gestures with a 90.60% and 99.27% accuracy for user-independent and user-dependent models, respectively. Furthermore, the ring exhibits promising performance when worn on any finger. Ring-a-Pose enables the future of smart rings to track and recognize hand poses using relatively low-power acoustic sensing.