Dr. Zhao Zhao
Research Overview
Dr. Zhao Zhao’s research explores how wearable technologies, human–computer interaction, and artificial intelligence can be leveraged to better understand, support, and enhance human behavior. Her work focuses on integrating wearable sensing into everyday life to enable quantified-self and gamified applications that promote physical and mental well-being, while studying how personalized user models and recommendation systems influence engagement and behavior change.
A central theme of her research is the use of physiological signals from wearable sensors to objectively capture human emotions and experiences in interactive systems. By developing standardized methods for emotion recognition and user-state inference, Dr. Zhao advances more reliable, continuous, and human-centered evaluation of novel HCI systems. Her work also extends to human–robot interaction, where she investigates how equipping humans with wearable sensors can serve as an effective communication bridge between humans and robots, reducing robot complexity while enabling safer, more legible, and more adaptive collaboration.
Research Keywords
Wearable computing | Human–computer interaction (HCI) | Quantified self | Gamification | Physiological sensing | Emotion recognition | User modeling | Personalized recommendation systems | Human–robot interaction (HRI) | AI and machine learning | Sensor-based interaction | Behavioral analytics
Dr. Zhao's Projects
The Heartbeat of Engagement: Exploring Young Children's Interactions with a Reading Pen, Robot, and Human through Physiological Signals
This study explored how different reading modes—a human reader, a reading robot, and a reading pen—affect young children’s engagement and comprehension. Using physiological measures and comprehension tests, we found that the reading robot kept children engaged the longest and supported the highest learning outcomes. The robot balanced emotional arousal and stress, helping children process and retain information more effectively. Findings also showed that family technology use positively influenced engagement and comprehension with the robot. This research highlights how interactive technologies can complement traditional learning and support early childhood education.