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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. 

We present HPSpeech, a silent speech interface for commodity headphones. HPSpeech utilizes the existing speakers of the headphones to emit inaudible acoustic signals. The movements of the temporomandibular joint (TMJ) during speech modify the reflection pattern of these signals, which are captured by a microphone positioned inside the headphones. To evaluate the performance of HPSpeech, we tested it on two headphones with a total of 18 participants. The results demonstrated that HPSpeech successfully recognized 8 popular silent speech commands for controlling the music player with an accuracy over 90%. While our tests use modified commodity hardware (both with and without active noise cancellation), our results show that sensing the movement of the TMJ could be as simple as a firmware update for ANC headsets which already include a microphone inside the hear cup. This leaves us to believe that this technique has great potential for rapid deployment in the near future. We further discuss the challenges that need to be addressed before deploying HPSpeech at scale. 

In this paper, we introduce PoseSonic, an intelligent acoustic sensing solution for smartglasses that estimates upper body poses. Our system only requires two pairs of microphones and speakers on the hinges of the eyeglasses to emit FMCW-encoded inaudible acoustic signals and receive reflected signals for body pose estimation. Using a customized deep learning model, PoseSonic estimates the 3D positions of 9 body joints including the shoulders, elbows, wrists, hips, and nose. We adopt a cross-modal supervision strategy to train our model using synchronized RGB video frames as ground truth. We conducted in-lab and semi-in-the-wild user studies with 22 participants to evaluate PoseSonic, and our user-independent model achieved a mean per joint position error of 6.17 cm in the lab setting and 14.12 cm in semi-in-the-wild setting when predicting the 9 body joint positions in 3D. Our further studies show that the performance was not significantly impacted by different surroundings or when the devices were remounted or by real-world environmental noise. Finally, we discuss the opportunities, challenges, and limitations of deploying PoseSonic in real-world applications. 

A person's emotional state can strongly influence their ability to achieve optimal task performance. Aiming to help individuals manage their feelings, different emotion regulation technologies have been proposed. However, despite the well-known influence that emotions have on task performance, no study to date has shown if an emotion regulation technology can also enhance user's cognitive performance in the moment. In this paper, we present BoostMeUp, a smartwatch intervention designed to improve user's cognitive performance by regulating their emotions unobtrusively. Based on studies that show that people tend to associate external signals that resemble heart rates as their own, the intervention provides personalized haptic feedback simulating a different heart rate. Users can focus on their tasks and the intervention acts upon them in parallel, without requiring any additional action. The intervention was evaluated in an experiment with 72 participants, in which they had to do math tests under high pressure. Participants who were exposed to slow haptic feedback during the tests decreased their anxiety, increased their heart rate variability and performed better in the math tests, while fast haptic feedback led to the opposite effects. These results indicate that the BoostMeUp intervention can lead to positive cognitive, physiological and behavioral changes.