Augmented feedback, virtual reality and robotics for designing new rehabilitation methods

Abstract

Neuro-rehabilitation is currently undergoing a technological revolution! Groups of engineers and rehabilitation specialists are working on designing and testing a great variety of rehabilitation devices and systems. The reason for this is that, although it is generally accepted that rehabilitation improves outcome after stroke, patients are still left with impairments causing various levels of handicap and limiting their integration in community life. Comparison of traditional rehabilitation techniques has failed to show superiority of one over another (15), and concepts for rehabilitation have been changing over the last 20 years, with the biggest change being evaluation. There is a move to make rehabilitation techniques more evidence based. As such, numerous research teams have set about to create more effective rehabilitation techniques based on principles of motor control and learning and incorporating new technology to fulfil the principal goals of rehabilitation: increased functional ability and increased participation in the community. The aim of this chapter is to discuss applications for augmented feedback (AF) in rehabilitation of motor skills of patients with neurological disorders, in particular within virtual reality (VR) environments associated or not with mechatronic devices (robotics). First, we will examine some motor learning principles relevant to rehabilitation and how AF fits into these concepts. We will then go on to review applications of AF used for rehabilitation of specific movement parameters. We will also discuss the use of feedback distortion to manipulate action-perception coupling and systems based on movement observation.