Abstract
Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics. In order to make use of the prostheses’ full functionality, it is essential to study efficient ways of high dimensional myoelectric control. Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would act naturally. But can a similar control scheme be translated into real-time operation of a dexterous robotic hand? We found that despite different degrees of freedom in the effector output, the learning process for controlling a robotic hand was surprisingly similar to that for a virtual two-dimensional cursor. Control signals were derived from the EMG in two different ways, with a linear and a Bayesian filter, to test how stable user intentions could be conveyed through them. Our analysis indicates that without visual feedback, control accuracy benefits from filters that reject high EMG amplitudes. In summary, we conclude that findings on myoelectric control principles, studied in abstract, virtual tasks can be transferred to real-life prosthetic applications.Electronic supplementary materialThe online version of this article (doi:10.1007/s10439-013-0876-5) contains supplementary material, which is available to authorized users.
Highlights
Improvements in robotics have advanced the design of hand prostheses to rival the functionality of a human hand
If the limitation of myoelectric sources can be overcome, humans are well able to use multiple muscles for myoelectric control, as has been demonstrated in healthy subjects: Radhakrishnan et al.[13] showed that subjects could learn to control a cursor in two dimensions on a computer screen through EMG activity recorded during isometric contractions of multiple upper-limb muscles
We present a translation of a myoelectric cursor control paradigm to direct proportional control of a dexterous hand prosthesis
Summary
Improvements in robotics have advanced the design of hand prostheses to rival the functionality of a human hand. If the limitation of myoelectric sources can be overcome, humans are well able to use multiple muscles for myoelectric control, as has been demonstrated in healthy subjects: Radhakrishnan et al.[13] showed that subjects could learn to control a cursor in two dimensions on a computer screen through EMG activity recorded during isometric contractions of multiple upper-limb muscles. In their setup, the magnitude of the EMG from six sites on hand and arm proportionally controlled cursor position in one of six directions each, with the momentary position of the cursor determined by the vector sum over all six contributions. It has been shown that control of hand muscles is
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
