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About

Xujie Lang is a researcher dedicated to advancing human-robot collaboration, particularly for elderly care. Their work focuses on developing intelligent systems that enable seamless interaction between humans and machines through multimodal intention recognition. Lang’s key contributions include the HMMCF algorithm, a human-computer collaboration framework that uses reverse active fusion of multimodal intentions to enhance responsiveness and accuracy in assistive robots. This work, with 15 citations, addresses critical gaps in collaborative capability for escort robots. Lang also proposed the MES (Helping Elderly Escort Interactive System) and HRCS_EE (Human-Robot Collaboration System for the Elderly), both designed to tackle the urgent challenges of an aging population by integrating intuitive, reverse-active intention detection. These systems aim to make robots more adaptive and empathetic partners for elderly users, improving quality of life through technology. Lang’s research, though early in its citation impact, is pioneering in its focus on reverse active fusion—a novel approach that could redefine how robots understand and respond to human needs. Their work stands out for its practical, user-centered design, targeting real-world problems in elder care with innovative, interactive solutions.