Abstract

By 2050, older adults will make up about 22% of the global population, driving an urgent need for accessible and reliable health technologies. In this talk, I will present our work on intelligent mobile systems designed for older adults. The first leverages compact AI-enabled radios for cardiovascular monitoring, including blood pressure. The second is an ambient sensing system that uses smart devices to detect emergent, life-threatening events such as cardiac arrest. The third enables low-cost health screening using everyday earphones and wireless earbuds. Through these examples, I will show how computational and sensing techniques that generalize across hardware and operate in real-world environments can address pressing societal challenges.

Bio Justin is an assistant professor in CS and ECE at Carnegie Mellon University, where he directs the Semantic Signals Lab. His work focuses on AI-enabled digital health systems with a focus on wireless and mobile technologies. His innovations include using smartphone sensors for blood clot testing, training smart speakers to detect cardiac arrests, wireless earbuds that screen for newborn hearing loss, and detection of middle ear fluid using active sonar on smartphones and a paper cone. He earned his PhD at the University of Washington and his work has been recognized by CACM and SIGMOBILE Research Highlights, SIGMOBILE Doctoral Dissertation Award Runner Up, and a IEEE Pervasive Computing Emerging Rockstar feature.