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About

Yongkang Tang is a pioneering researcher in human-robot interaction, specializing in multimodal gesture recognition and emotion-aware communication systems. His work focuses on enabling casual, human-like communication between robots and humans by fusing data from cameras and 3D accelerometers. Tang’s most influential contribution is his development of fuzzy logic and Choquet integral-based methods for gesture recognition, which integrate visual and motion sensor data to achieve robust, real-time emotion detection. His 2010 paper on gesture recognition using acceleration sensors and images (13 citations) and his 2011 work on multimodal gesture recognition via Choquet integral (9 citations) are foundational in this area. Tang also explored emotion recognition in music, applying strings music theory to mascot robot systems (2012, 6 citations). Later, he introduced the concept of “Deep Level Situation Understanding” (2015, 4 citations), a framework for multi-agent systems to interpret context beyond surface-level gestures, aiming for truly natural interaction. With a total of over 40 citations across his key works, Tang’s research bridges sensor fusion, fuzzy systems, and affective computing, offering practical pathways for robots to understand and respond to human emotional states in real time.