Chun Cao

Papers

Total Citations

H-Index

About

Chun Cao’s research lies at the intersection of human motion analysis and dependable self-adaptive software systems, bridging the physical and digital worlds. In biomechanical sensing, Cao pioneered the use of ground reflex pressure (GRF) signals from instrumented shoes for human walking pattern recognition, developing a kernel principal component analysis (KPCA) and support vector machine (SVM) framework that achieved robust classification under real-world conditions (16 citations). This work has implications for rehabilitation robotics and smart prosthetics. In software engineering, Cao tackled the critical challenge of runtime dependability in self-adaptive systems. By identifying the correlation between application failures and consistency failures, Cao proposed an “environment rematching” technique that enables systems to detect and repair faults that traditional approaches miss (12 citations). Further advancing this line, Cao developed a resynchronization method that realigns a system’s internal model with its changing environment after a failure, ensuring continued correct adaptation (8 citations). Together, these contributions form a cohesive vision: building systems—whether robotic or software—that can sense, adapt, and recover autonomously in unpredictable settings.