Papers
2
Total Citations
15
H-Index
2
About
Xiaokai Nie is a researcher at the forefront of integrating robotics with Traditional Chinese Medicine (TCM), specializing in human-robot interaction, sensor fusion, and edge computing. His most notable contribution lies in advancing robot-assisted TCM pulse diagnosis, where he developed an adaptive sensor fusion method to achieve accurate targeting of diagnostic positions on the wrist—a critical challenge in dynamic environments. This work, published in 2019, has garnered 9 citations and demonstrates how robotics can enhance the precision and reliability of ancient medical practices. Nie further expanded his impact by improving the Camshift algorithm for AGV vision-based tracking, leveraging edge computing to boost real-time performance in industrial applications. His 2021 paper on this topic has earned 6 citations, reflecting its relevance in autonomous navigation and manufacturing. By bridging biomedical engineering and intelligent systems, Nie’s research not only modernizes TCM diagnostics but also contributes to the broader field of adaptive robotics. His work exemplifies how interdisciplinary innovation can solve practical problems, making him a key figure in the convergence of healthcare and automation.
Research Focus
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Top Papers
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