Zhao Zhao

Education and Employment Background

Dr. Zhao Zhao received her PhD in Electrical and Computer Engineering from Carleton University in 2019. She then held a position as a Postdoctoral Fellow at the Faculty of Information (iSchool) at the University of Toronto from 2019 to 2023. Following this, she worked as an Assistant Professor at McMaster University from 2023 to 2024, before joining the School of Computer Science at the University of Guelph. 

Research Themes

The primary objective of my research is to leverage emerging technologies to promote healthier lifestyles and enhance functionality in a future where humans coexist with multiple intelligent entities. Aligned with this objective, my research primarily focuses on exploring the use of wearable-based physiological sensors to gain a deeper understanding of individuals and personalize their daily interactions with the world.  

Current Research Themes: 

1. Wearables for Gamified and Quantified-Self Applications  

The "Quantified Self" movement embraces self-tracking through technology to enhance physical and mental performance. Wearable devices play a key role in this, seamlessly integrating with daily activities to create interactive, immersive experiences while empowering users to monitor and optimize their health and well-being. I explore how evolving wearables positively impact human lifestyles. This includes optimizing gamified recommendation systems and experimenting with various features in player models to understand their effects on user behavior.  

2. What Our Body Tells Us  

One of the critical challenges of evaluating user interactions with novel HCI systems lies in measuring emotions. Traditional methods include self-report measures, which face criticisms for potentially drawing attention to the experiment's focus, failing to capture low-intensity emotions, and lacking validity.  

Physiological measures are increasingly used by HCI researchers due to their dynamic and continuous nature, providing a deeper understanding of user experiences despite setup and invasiveness concerns. Flexible, wearable sensing devices can yield important information about the underlying physiology of a human subject for applications. I aim to standardize methods enabling HCI researchers to effectively recognize human emotions using wearable sensors.  

3. Wearable Technology in Human-Robot Interaction (HRI)  

With the rapid advancements in sensor technology and the remarkable progress in AI and machine learning, robots are evolving from simple machines to collaborative entities with cognitive capabilities. Any robot interacting with humans must fulfill a range of fundamental requirements, such as compliance, safety, and legibility of motions and actions. All the sensors, visualizations, and legible actions to achieve these requirements contribute to the complexity and cost of robots designed for human interaction.  

On the other hand, humans are already progressing towards being equipped with sensors and technologies. One potential solution to address the complexity of robots is for humans to adapt themselves with wearables. These wearables would act as a bridge between human-interpretable data and robot-interpretable data. By moving away from an anthropocentric view of HRI, my research explores how equipping humans with sensors and hardware can enhance communication between humans and robots.