Modeling and learning human behavior in human-centric cyber-physical systems

Cyber-Physical System (CPS) integrates sensing, computation, and control with the physical process, which has transformed how the physical world interacts with the digital world. With billions of devices interconnected worldwide and the rapid progress of AI models such as large language models, CPS has greatly enhanced the efficiency and automation of physical systems, such as healthcare and logistics. However, as CPS evolves toward increasingly interactive and adaptive architectures, humans are no longer passive recipients of system outputs but active participants in sensing, learning, and decision-making loops. This transformation introduces a new paradigm, Human-Centric Cyber-Physical Systems (HCPS), where human behavior plays a critical role in shaping system intelligence. Despite their growing importance, the human component in CPS remains largely underexplored compared to the physical and cyber aspects. This dissertation investigates how to model, learn, and leverage human behavior to improve the intelligence and performance of large-scale HCPS. Specifically, we will introduce three representative systems. (i) GenHAR learns fine-grained human motion dynamics from wearable IMU sensor data, enabling cross-domain behavior recognition without target-domain supervision. By modeling sensor attention in the frequency domain, GenHAR achieves 9.97% higher accuracy and a significant reduction in computational costs compared to state-of-the-art baselines. (ii) CoMiner learns coarse-grained human mobility patterns from large-scale GPS trajectories of over delivery couriers, achieving 95.1% accuracy and over 20% improvement compared to existing methods. (iii) Finally, CarbonSense demonstrates the societal impact of HCPS by quantifying human behavior-driven carbon emissions across nationwide logistics operations. These systems are evaluated with real-world datasets and have shown promising results in modeling large-scale human behavior and supporting practical applications.