Kay‐Soon Low
Papers
5
Total Citations
48
H-Index
5
About
Kay‐Soon Low is a leading researcher in robotics, control systems, and sensor technologies, with a focus on enhancing human–machine interaction and autonomous navigation. His work spans impedance control for robotic manipulators, where he pioneered neural network-based environment estimation to enable precise force tracking—a critical advancement for safe and adaptive robot operation in unstructured settings. Low also developed a combined impedance/direct control strategy that improves force tracking accuracy under external disturbances, demonstrating practical impact in rehabilitation robotics. In the domain of localization, he proposed an accurate 3D UWB hyperbolic localization method using iterative Taylor-series estimation, achieving robust indoor positioning in multipath environments—a key enabler for autonomous systems and IoT applications. His contributions extend to biomedical devices, including the design of a portable active orthotic device for knee assistance, and to advanced imaging, where he developed an 8-stage time delay integration CMOS image sensor with on-chip polarization pixels for improved machine vision through transparent surfaces. With over 48 citations across his most-cited works, Low’s research continues to influence robotics, sensor fusion, and assistive technology, bridging theoretical innovation with real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 5Combined Impedance/Direct Control of Robot Manipulators5 citations · 2006