1. Rehabilitation Engineering
Over the last decade researchers have been applying learning control to help stroke patients recover movement. Learning algorithms are used to adjust the electrical stimulation applied to the muscles of stroke patients. The stimulation is precisely controlled in order to assist patients’ completion of functional reaching tasks like pushing a light switch or picking up a cup. The controllers used to do this learn from experience how much help to give each patient to make the therapy as effective as possible.
The team, led by Dr Chris Freeman at Southampton University in the UK, has shown that these technologies have significant clinical effectiveness and the aim now is to create systems that patients can use in their own homes. By exploiting the effectiveness of learning control, the aim is to soon combine these elements to provide a system that can provide treatment to the 15 million people worldwide who suffer from a stroke each year.
C. T. Freeman. Control System Design for Electrical Stimulation in Upper Limb Rehabilitation. Springer International Publishing, December 2015
C. T. Freeman et al. Iterative learning control in healthcare electrical stimulation and robotic-assisted upper limb stroke rehabilitation. IEEE Control Systems Magazine, 32, (1), 18-43, 2012.
C. T. Freeman et al. Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation (SpringerBriefs in Electrical and Computer Engineering), Springer, 2015.