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Adaptive artificial limbs: a real-time approach to prediction and anticipation

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7 Author(s)
Patrick M. Pilarski ; Department of Computing Science, University of Alberta, Edmonton, T6G 2E8 Alberta, Canada ; Michael Rory Dawson ; Thomas Degris ; Jason P. Carey
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Predicting the future has long been regarded as a powerful means to improvement and success. The ability to make accurate and timely predictions enhances our ability to control our situation and our environment. Assistive robotics is one prominent area in which foresight of this kind can bring improved quality of life. In this article, we present a new approach to acquiring and maintaining predictive knowledge during the online ongoing operation of an assistive robot. The ability to learn accurate, temporally abstracted predictions is shown through two case studies: 1) able-bodied myoelectric control of a robot arm and 2) an amputee's interactions with a myoelectric training robot. To our knowledge, this research is the first demonstration of a practical method for real-time prediction learning during myoelectric control. Our approach therefore represents a fundamental tool for addressing one major unsolved problem: amputee-specific adaptation during the ongoing operation of a prosthetic device. The findings in this article also contribute a first explicit look at prediction learning in prosthetics as an important goal in its own right, independent of its intended use within a specific controller or system. Our results suggest that real-time learning of predictions and anticipations is a significant step toward more intuitive myoelectric prostheses and other assistive robotic devices.

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IEEE Robotics & Automation Magazine  (Volume:20 ,  Issue: 1 )