Yunbo Li

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About

Yunbo Li is a researcher at the forefront of human–machine interaction and assistive robotics, with a primary focus on intelligent prosthetics and service robots for the disabled and elderly. His most-cited work introduces a CAM-MR-MS based gesture recognition method using surface electromyography (sEMG), which significantly advances the field by incorporating an adaptive channel selection technique. This innovation simplifies the sEMG measurement process, reducing computational complexity while maintaining high recognition accuracy—a critical step toward practical, real-time control of prosthetic limbs and assistive devices. By streamlining the sensor setup, Li’s approach enhances the usability and accessibility of gesture-based control systems, directly addressing the needs of individuals with motor impairments. With two citations already for a 2025 publication, his work is gaining early recognition for its potential to improve quality of life. Li’s contributions lie at the intersection of biomedical signal processing, machine learning, and robotics, offering a scalable solution that bridges the gap between laboratory research and real-world assistive technology.