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In this paper, we present EatingTrak, an AI-powered sensing system using a wrist-mounted inertial measurement unit (IMU) to recognize eating moments in a near-free-living semi-wild setup. It significantly improves the SOTA in time resolution using similar hardware on identifying eating moments, from over five minutes to three seconds. Different from prior work which directly learns from raw IMU data, it proposes intelligent algorithms which can estimate the arm posture in 3D in the wild and then learns the detailed eating moments from the series of estimated arm postures. To evaluate the system, we collected eating activity data from 9 participants in semi-wild scenarios for over 113 hours. Results showed that it was able to recognize eating moments at three time-resolutions: 3 seconds and 15 minutes with F-1 scores of 73.7% and 83.8%, respectively. EatingTrak would introduce new opportunities in sensing detailed eating behavior information requiring high time resolution, such as eating frequency, snack-taking, on-site behavior intervention. We also discuss the opportunities and challenges in deploying EatingTrak on commodity devices at scale.

In this paper, we present BodyTrak, an intelligent sensing technology that can estimate full body poses on a wristband. It only requires one miniature RGB camera to capture the body silhouettes, which are learned by a customized deep learning model to estimate the 3D positions of 14 joints on arms, legs, torso, and head. We conducted a user study with 9 participants in which each participant performed 12 daily activities such as walking, sitting, or exercising, in varying scenarios (wearing different clothes, outdoors/indoors) with a different number of camera settings on the wrist. The results show that our system can infer the full body pose (3D positions of 14 joints) with an average error of 6.9 cm using only one miniature RGB camera (11.5mm x 9.5mm) on the wrist pointing towards the body. Based on the results, we disscuss the possible application, challenges, and limitations to deploy our system in real-world scenarios.

This paper presents EarIO, an AI-powered acoustic sensing technology that allows an earable (e.g., earphone) to continuously track facial expressions using two pairs of microphone and speaker (one on each side), which are widely available in commodity earphones. It emits acoustic signals from a speaker on an earable towards the face. Depending on facial expressions, the muscles, tissues, and skin around the ear would deform differently, resulting in unique echo profiles in the reflected signals captured by an on-device microphone. These received acoustic signals are processed and learned by a customized deep learning pipeline to continuously infer the full facial expressions represented by 52 parameters captured using a TruthDepth camera. Compared to similar technologies, it has significantly lower power consumption, as it can sample at 86 Hz with a power signature of 154 mW. A user study with 16 participants under three different scenarios, showed that EarIO can reliably estimate the detailed facial movements when the participants were sitting, walking or after remounting the device. Based on the encouraging results, we further discuss the potential opportunities and challenges on applying EarIO on future ear-mounted wearables.

This paper presents SpeeChin, a smart necklace that can recognize 54 English and 44 Chinese silent speech commands. A customized infrared (IR) imaging system is mounted on a necklace to capture images of the neck and face from under the chin. These images are first pre-processed and then deep learned by an end-to-end deep convolutional-recurrent-neural-network (CRNN) model to infer different silent speech commands. A user study with 20 participants (10 participants for each language) showed that SpeeChin could recognize 54 English and 44 Chinese silent speech commands with average cross-session accuracies of 90.5% and 91.6%, respectively. To further investigate the potential of SpeeChin in recognizing other silent speech commands, we conducted another study with 10 participants distinguishing between 72 one-syllable nonwords. Based on the results from the user studies, we further discuss the challenges and opportunities of deploying SpeeChin in real-world applications.

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.

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