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 find efficient ways to control their multiple actuators. 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. We investigated whether a similar control scheme, using signals from four hand muscles, could be adopted for real-time operation of a dexterous robotic hand. Despite different mapping strategies, learning to control the robotic hand over time was surprisingly similar to the learning of two-dimensional cursor control.
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