Papers
6
Total Citations
316
H-Index
4
About
Dr. K. Ming Chan is a leading researcher in neural engineering and assistive robotics, whose work focuses on restoring function and sensation for upper-limb amputees. His most influential contribution is the development of a novel targeted sensory reinnervation technique, detailed in a highly cited 2014 paper (145 citations), which uses intraoperative somatosensory evoked potentials to map individual nerve fascicles, enabling more precise and natural sensory feedback after transhumeral amputation. Chan has also pioneered the integration of real-time machine learning into myoelectric prosthesis control (62 citations), creating adaptive artificial limbs that predict user intent to reduce cognitive load and improve dexterity. His 2013 work on prediction and anticipation in assistive robotics (64 citations) further underscores his commitment to seamless human-machine partnership. Chan’s research bridges surgical innovation and computational intelligence, offering amputees not just mechanical replacement but intuitive, sensory-rich interaction with their environment. His achievements have advanced the field of bionic rehabilitation, making him a key figure in the quest for truly lifelike prosthetic function.
Research Focus
Key Achievements
Top Papers
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- 3Application of real-time machine learning to myoelectric prosthesis control62 citations · 2015
- 4Updates in Targeted Sensory Reinnervation for Upper Limb Amputation42 citations · 2014
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