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About

Zhenzhi Ying is pioneering the intersection of surgical robotics and neural-controlled prosthetics, with a focus on making medical interventions safer and more intuitive. His most-cited work, "Bone Milling: On Monitoring Cutting State and Force Using Sound Signals" (2022, 20 citations), addresses a critical challenge in orthopedic and neurosurgical procedures: preventing tissue and tool damage during bone milling. By using sound signals to monitor cutting states in real time, Ying offers a non-invasive, cost-effective alternative to traditional force-based monitoring, potentially reducing surgery time and improving patient outcomes. More recently, he has advanced the field of neural decoding with "Real-time Dexterous Prosthesis Hand Control by Decoding Neural Information Based on EMG Decomposition" (2024, 3 citations), tackling the long-standing problem of restoring fine motor control in amputees through more precise interpretation of myoelectrical signals. His work on "Integrating musculoskeletal simulation and machine learning" (2024, 3 citations) further demonstrates his commitment to personalized assistive technologies, creating hybrid models for ankle-foot exoskeletons that adapt to individual gait patterns. With a growing citation impact and a focus on translating complex neural and biomechanical signals into practical clinical tools, Ying is establishing himself as a rising leader in rehabilitation engineering and surgical assistance.