Changbing Chen

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About

Changbing Chen is a leading researcher at the forefront of exoskeleton robotics and human–machine interaction, with a primary focus on motion intention recognition and control strategies for upper limb assistive technologies. Their most influential work, a comprehensive review on surface electromyography (sEMG) for exoskeleton robots, has garnered 24 citations and addresses the critical challenge of interpreting human movement intent to enable seamless robotic assistance—a vital need for aging populations and rehabilitation medicine. Chen further advanced the field with a detailed analysis of control strategies for upper limb exoskeletons (14 citations), highlighting innovations in high-precision sensing and adaptive control that bridge robotics with human physiology. Notably, Chen also developed an embedded sEMG signal acquisition device (10 citations), a practical hardware solution that enables real-time, low-noise capture of muscle signals, pushing toward more intuitive and anticipatory human–robot collaboration. With a growing citation impact and a focus on translating complex biosignal processing into deployable systems, Chen’s work is shaping the next generation of wearable robots—making them smarter, safer, and more responsive to users’ needs in clinical and daily living contexts.