Automated dietary monitoring is essential for gaining insights into eating behaviors, especially for managing chronic conditions such as obesity, diabetes, and hypercholesterolemia. Earable-based inertial sensing has been found promising for detecting chewing and eating activities; however, further insights like what, when, and how much is being eaten are crucial information for effective dietary assessment. Therefore, we propose BiteSense, an earable-based system that leverages inertial sensors (IMU) to monitor food intake and classify various food types. Using a hierarchical classification model, the system analyzes masticatory kinematics to detect food states, textures, nutritional value, and cooking methods, ultimately identifying specific foods consumed, as well as estimating food intake amount and meal type. A semi-controlled user study involving 38 participants from diverse backgrounds demonstrated the system's high accuracy, with an F1 score of 0.86 for detecting the masticatory process using a leave-one-subject-out (LOSO) approach, while exhibiting significant superiority over benchmark algorithms in extensive experiments by 8-12%.

This paper presents a framework called enGauge that leverages ear-based inertial sensing to continuously monitor listener focus levels in online meetings and provide feedback to the speaker about audience engagement. This allows for the identification of the onset of several neurodevelopmental disorders, including attention deficit hyperactivity disorder (ADHD), and can help to improve the effectiveness of online meetings by allowing speakers to adjust their speaking pace and style based on audience engagement. We explore a contrastive learning-based approach coupled with a judicial selection of anchor events from the meeting contents to model the system. enGauge can detect patterns or shifts in behavior and focus levels of passive listeners to accurately identify changes in focus. Results from a user study with 38 participants showed an overall F1-score of 0.89 for detecting passive listeners’ focus levels. Our study suggests that ear-based inertial sensing has the potential to be a valuable tool for the early detection and monitoring of several neurodevelopmental disorders among individuals.