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Automated voice calls with health information are a proven method for disseminating maternal and child health information among beneficiaries and are deployed in several programs around the world. However, these programs often suffer from…
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits…
Maintaining thermal comfort in shared indoor environments remains challenging, as centralized HVAC systems are slow to adapt and standardized to group norms. Cold exposure not only reduces subjective comfort but can impair cognitive…
Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless communication…
Auditory working memory is essential for various daily activities, such as language acquisition, conversation. It involves the temporary storage and manipulation of information that is no longer present in the environment. While extensively…
We present a robust data-driven control scheme for an unknown linear system model with bounded process and measurement noise. Instead of depending on a system model in traditional predictive control, a controller utilizing data-driven…
Sensory earables have evolved from basic audio enhancement devices into sophisticated platforms for clinical-grade health monitoring and wellbeing management. This paper introduces OmniBuds, an advanced sensory earable platform integrating…
We introduce MunchSonic, an AI-powered active acoustic sensing system integrated into eyeglasses to track fine-grained dietary actions. MunchSonic emits inaudible ultrasonic waves from the eyeglass frame, with the reflected signals…
Large reasoning models (LRMs) achieve state-of-the-art performance by generating long chains-of-thought, but often waste computation on redundant reasoning after the correct answer has already been reached. We introduce Early-Stopping for…
Within the landscape of inference-time scaling methods for foundation models, a width-based approach to scaling -- which involves the insertion of <pause> tokens in the input stream to delay model responses -- offers a unique advantage by…
Respiratory rate (RR) monitoring is integral to understanding physical and mental health and tracking fitness. Existing studies have demonstrated the feasibility of RR monitoring under specific user conditions (e.g., while remaining still,…
Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the…
Accurate estimation of the human circadian phase plays an important role in personalized health monitoring, but most existing wearable-based approaches operate retrospectively and require full circadian cycle recordings, leading to high…
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. Although several methods to estimate nutrient intake have been developed, there is still a clear…
This paper presents a Q-learning framework for learning optimal locomotion gaits in robotic systems modeled as coupled rigid bodies. Inspired by prevalence of periodic gaits in bio-locomotion, an open loop periodic input is assumed to (say)…
We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: it…
Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible? Early decision-making is critical in many applications, e.g. monitoring…
Flying robots such as the quadrotor could provide an efficient approach for medical treatment or sensor placing of wild animals. In these applications, continuously targeting the moving animal is a crucial requirement. Due to the…
Large Reasoning Language Models (LRLMs) demonstrate impressive capabilities on complex tasks by utilizing long Chain-of-Thought reasoning. However, they are prone to overthinking, which generates redundant reasoning steps that degrade both…
Conventional scalp-based EEG systems are cumbersome to use, requiring extensive setup, restrictive wiring, and conductive gels that can dry out and limit long-term monitoring, while also carrying social stigma. As a result, there is…