Abstract and Proportional Myoelectric Control for Multi-Fingered Hand Prostheses
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Abstract and Proportional Myoelectric Control for Multi-Fingered Hand Prostheses TOBIAS PISTOHL,1 CHRISTIAN CIPRIANI,2 ANDREW JACKSON,1 and KIANOUSH NAZARPOUR1 1 Institute of Neuroscience, Newcastle University, Henry Wellcome Building, Framlington Place, Newcastle upon Tyne NE2 4HH, UK; and 2The BioRobotics Institute, Scoula Superiore Sant’Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, PI, Italy
(Received 25 March 2013; accepted 24 July 2013) Associate Editor Anastasios G. Bezerianos oversaw the review of this article.
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. Keywords—Electromyography, Robotic Hands, Prosthetic Control, Virtual Control.
ABBREVIATIONS APB 1DI
Abductor pollicis brevis muscle First dorsal interosseous muscle
Address correspondence to Tobias Pistohl, Institute of Neuroscience, Newcastle University, Henry Wellcome Building, Framlington Place, Newcastle upon Tyne NE2 4HH, UK. Electronic mail: [email protected], [email protected]
3DI ADM DoA
Third dorsal interosseus muscle Abductor digiti minimi muscle Direction of action
INTRODUCTION Improvements in robotics have advanced the design of hand prostheses to rival the functionality of a human hand. Some designs with multiple degrees of freedom are now entering the market for patients, like the i-limb (Touch Bionics, Livingston, UK), the bebionic (RSLSteeper, Leeds, UK) or the Michelangelo hand (Ottobock, Duderstadt, Germany). However, myoelectric control of current hand prostheses cannot compete with the dexterity and versatility of a human hand. One limitation is that the measurement of reliable and sufficiently independent signals of surface electromyography (EMG) from several muscles is difficult in amputees. Therefore, current commercial implementations of hand prostheses usually employ only one or two EMG channels and an on
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