Kuanlin Wang

Ministry of Education of the People's Republic of China, China University of Geosciences

Papers

Total Citations

109

H-Index

About

Kuanlin Wang is a leading researcher at the intersection of affective computing and human–robot interaction (HRI), with a primary focus on enabling robots to perceive and express emotions. Wang’s major contributions lie in two complementary areas: multimodal emotion recognition and expressive robotic motion. In their highly cited 2022 work (106 citations), Wang pioneered a *K*-means clustering-based Kernel Canonical Correlation Analysis algorithm that fuses audio and visual signals for robust emotion recognition, significantly improving accuracy in noisy HRI environments. Complementing this perceptual work, Wang developed a novel framework for robotic arm trajectory generation that maps emotional states—such as happiness or sadness—into kinematic features, allowing robots to produce expressive, human-like gestures. This dual approach—teaching machines both to “read” and to “show” emotion—has positioned Wang as a key innovator in socially assistive robotics. Their research not only advances fundamental algorithms in multimodal fusion and motion planning but also has practical implications for therapeutic robots and collaborative industrial assistants. With a growing citation impact and a clear trajectory toward more natural human–robot communication, Kuanlin Wang’s work is essential reading for anyone interested in building emotionally intelligent machines.