Here is a summary of my research interests.
Acquiring a new skill, for example learning to use chopsticks, requires accurate motor commands to be sent from the brain to the hand, and reliable sensory feedback from the hand to the brain. Over time and with training, the brain learns to handle this two-way communication flexibly and efficiently. Inspired by this sensorimotor interplay, my research is guided by a conviction that progress in prosthetic limb control is best achieved through a strong synergy of motor learning and sensory feedback.
I study the interaction of neural and behavioural processes that control the hand movements to ultimately innovate prosthetic control solutions that users would find fit for purpose.
Human-in-the-loop Machine Learning
How can we design a prosthesis controller that embeds the benefits of both machine and human learning methodologies?
We predict that in a setting that the human user and the machine learn collaboratively, we will see: 1) more accurate estimation of the movement intents; 2) faster user adaptation; 3) more reliable retention of internal model, and 4) sustainable stakeholder engagement in this user-centred approach. We aim to verify the accuracy of these predictions.