Subject Area
Computer Science
Abstract
Smart devices are becoming increasingly integrated into our daily lives, offering convenience and immersive experiences. However, the existing authentication methods for these devices are insecure and vulnerable to various attacks. For example, traditional biometric authentication methods (such as Face ID, fingerprint, and hand biometrics) can be easily forged using advanced 3D technologies and leveraged for replay attacks. Beyond security, smart devices also pose significant safety risks. For example, while smartphones enable easy communication, the constant need to check notifications distracts drivers, creating dangerous driving conditions. Moreover, although smart devices like smartphones and smartwatches are widely used in healthcare, they often require active user input, which can limit their effectiveness in continuous monitoring scenarios. While Artificial Intelligence (AI) has made significant contributions across various societal domains, effectively applying it to solve real-world problems for the greater social good remains a challenge. My research focuses on combining the power of AI with unobtrusive sensing technologies to address this gap. Specifically, I aim to enhance security, improve safety, and make smart devices more seamless and user-friendly in healthcare applications.
Degree Date
Summer 8-5-2025
Document Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science
Advisor
Chen Wang
Number of Pages
170
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Wang, Ruxin, "AI-Powered Unobtrusive Sensing for Advancing Social Good" (2025). Computer Science and Engineering Theses and Dissertations. 51.
https://scholar.smu.edu/engineering_compsci_etds/51
