Biological Inspired Control and Machine Learning for Clinical Rehabilitation and Engineering Systems

Understanding how humans control movement and posture has long been a focus for scientists and engineers.

However, the underlying mechanisms of human motor control and its relation to adaptation and learning remain controversial.

Recently, researchers at the University of Glasgow have developed an intermittent control approach that combines open-loop predictive control and intermittent closed-loop control. This approach aims to explain human-like motor adaptation and learning abilities.

Further investigating intermittent control principles of human motor learning and adaptation could enable the design of more effective and naturalistic rehabilitation approaches using neural prostheses (using electrical muscle stimulation), since rehabilitation fundamentally requires relearning motor skills.