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The state of underwater networking today is similar to ARPANET back in the 1970s, when only a select few people with expensive hardware resources had access to the technology. We present the first acoustic system that brings underwater networking capabilities to existing mobile devices like smartphones and smart watches. Our software-only solution leverages audio sensors, i.e., microphones and speakers, ubiquitous in today's devices, to enable acoustic underwater communication between mobile devices. To achieve this, we design a communication system that adapts in real-time to differences in frequency responses across mobile devices, changes in multipath and noise levels at different locations and dynamic channel changes due to mobility. We evaluate our system in six different real-world underwater environments in the presence of boats, ships and people fishing and kayaking. With the release of Apple Watch Ultra that is specifically designed for underwater settings, our software-based approach has the potential to democratize underwater networking capabilities by making them widely available to anyone with a mobile device.

Facial expressions are highly informative for computers to understand and interpret a person's mental and physical activities. However, continuously tracking facial expressions, especially when the user is in motion, is challenging. This paper presents NeckFace, a wearable sensing technology that can continuously track the full facial expressions using a neck-piece embedded with infrared (IR) cameras. A customized deep learning pipeline called NeckNet based on Resnet34 is developed to learn the captured infrared (IR) images of the chin and face and output 52 parameters representing the facial expressions. We demonstrated NeckFace on two common neck-mounted form factors: a necklace and a neckband (e.g., neck-mounted headphones), which was evaluated in a user study with 13 participants. The study results showed that NeckFace worked well when the participants were sitting, walking, or after remounting the device. We discuss the challenges and opportunities of using NeckFace in real-world applications.

Smart homes of the future are envisioned to have the ability to recognize many types of home activities such as running a washing machine, flushing the toilet, and using a microwave. In this paper, we present a new sensing technology, VibroSense, which is able to recognize 18 different types of activities throughout a house by observing structural vibration patterns on a wall or ceiling using a laser Doppler vibrometer. The received vibration data is processed and sent to a deep neural network which is trained to distinguish between 18 activities. We conducted a system evaluation, where we collected data of 18 home activities in 5 different houses for 2 days in each house. The results demonstrated that our system can recognize 18 home activities with an average accuracy of up to 96.6%. After re-setup of the device on the second day, the average recognition accuracy decreased to 89.4%. We also conducted follow-up experiments, where we evaluated VibroSense under various scenarios to simulate real-world conditions. These included simulating online recognition, differentiating between specific stages of a device's activity, and testing the effects of shifting the laser's position during re-setup. Based on these results, we discuss the opportunities and challenges of applying VibroSense in real-world applications.

C-Face (Contour-Face) is an ear-mounted wearable sensing technology that uses two miniature cameras to continuously reconstruct facial expressions by deep learning contours of the face. When facial muscles move, the contours of the face change from the point of view of the ear-mounted cameras. These subtle changes are fed into a deep learning model which continuously outputs 42 facial feature points representing the shapes and positions of the mouth, eyes and eyebrows. To evaluate C-Face, we embedded our technology into headphones and earphones. We conducted a user study with nine participants. In this study, we compared the output of our system to the feature points outputted by a state of the art computer vision library (Dlib) from a font facing camera. We found that the mean error of all 42 feature points was 0.77 mm for earphones and 0.74 mm for headphones. The mean error for 20 major feature points capturing the most active areas of the face was 1.43 mm for earphones and 1.39 mm for headphones. The ability to continuously reconstruct facial expressions introduces new opportunities in a variety of applications. As a demonstration, we implemented and evaluated C-Face for two applications: facial expression detection (outputting emojis) and silent speech recognition. We further discuss the opportunities and challenges of deploying C-Face in real-world applications.