Mark Ming‐Cheng Cheng
Papers
3
Total Citations
43
H-Index
3
About
Dr. Mark Ming-Cheng Cheng is a pioneer in the intersection of biomedical signal processing and assistive robotics. His research centers on harnessing machine learning to decode electromyography (EMG) signals, enabling intuitive, volitional control of upper-limb exoskeletons for rehabilitation. Dr. Cheng’s major contributions include developing real-time, multi-channel EMG processing frameworks that overcome challenges like noise and bio-variability—critical for translating lab-based algorithms into practical bionic devices. His most cited work (2023, 23 citations) demonstrates how machine learning empowers exoskeleton motion control directly from user intent. Earlier studies (2020, 15 citations) systematically compared artificial neural network architectures for hand motion pattern recognition, while his 2021 paper (5 citations) achieved real-time shoulder EMG control for rehabilitative exoskeletons, a notable step beyond off-line analysis. By merging robust signal processing with adaptive ML, Dr. Cheng is advancing human-machine interfaces that restore mobility and independence for individuals with motor impairments. His work stands at the forefront of a future where assistive robots respond seamlessly to human thought.
Research Focus
Key Achievements
Top Papers
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