Jan 31 (Fri) @ 10:00am: “Embedded AI and Sensing for Wellness, Fitness, and Health: Intelligent, Pervasive, Privacy-Aware Wearable Devices and AIoT Systems,” Jingping Nie, Columbia U.
Location: Engineering Science Building (ESB), Room 2001
Abstract
Recent advancements in smart devices and artificial intelligence have profoundly transformed personal fitness, health awareness, and self-care practices. These technologies, such as wearable mobile computing and Artificial Intelligence of Things (AIoT) systems, not only empower individuals with real-time insights into their body signals and health metrics but also increasingly support medical decision-making processes. Current methods face challenges in achieving optimal accessibility, affordability, usability, comfort, real-time responsiveness, scalability, and privacy awareness. This talk will explore how the integration of smart devices and intelligent systems into daily life redefines our understanding of wellness, fitness, and mental and physical health care, emphasizing the shift towards pervasive, preventive, proactive health management and personalized care solutions.
I will present my research on developing accessible wearable sensing platforms that bring continuous monitoring and interaction in emotion, fitness, and wellness to diverse communities. This includes exploring innovative form factors using commercial off-the-shelf sensors, computational fabrics, and emerging soft electronics to acquire multimodal signals, as well as designing efficient and privacy-aware systems and algorithms. Following this, I will introduce my research on model-driven analysis using computational methods including digital signal processing, machine learning, and foundation models for biosignal analysis and emotion detection with physiological signals and speech. Finally, I will demonstrate how common smart home devices can be repurposed for screening daily functioning, assessing mental health, monitoring physical health, and providing psychotherapeutic care through AI-enabled systems, in collaboration with licensed psychotherapists and psychiatrists.
Bio
Jingping Nie is a Ph.D. candidate in the Department of Electrical Engineering at Columbia University, advised by Professor Xiaofan (Fred) Jiang and Professor Matthias Preindl. She received her Master of Science degree (Honor Student) in Electrical Engineering from Columbia University in 2019 and her Bachelor of Science degree (magna cum laude with high honors) in Engineering Science from Smith College in 2017. Her research transforms everyday devices into intelligent healthcare solutions through the hardware-software co-design of human-centric intelligent and privacy-aware smart devices in Artificial Intelligence of Things (AIoT) systems. Her recent projects include creating wearable devices and AIoT systems for wellness, fitness, and health, as well as human-in-the-loop EV-interfaced microgrid optimizations. Her work has been published in various top-tier journals and conferences and received multiple distinctions, including the Best Paper and People’s Choice Demo at ACM MobiSys’24, Best Paper at IEEE ITEC’21, Best Demo at ACM/IEEE IPSN’20, and Best Demo Runner-up at ACM SenSys ’22. She is also the recipient of the 2023 Apple Scholars in AI/ML PhD fellowship and the 2023 EECS Rising Stars.
Hosted by: The ECE Department
Submitted by: Megan Ashley <mmashley@ucsb.edu>
