Craig Sherstan

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BACKGROUND Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. OBJECTIVES The goal of this study was to compare two switching-based methods of(More)
—We have developed a real-time machine learning approach for the collaborative control of a prosthetic arm. Upper-limb amputees are often extremely limited in the number of inputs they can provide to their prosthetic device, typically controlling only one joint at a time with the ability to toggle their control between the different joints of their(More)
Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions. While such adaptive agents may leverage engineered knowledge, they will require the capacity to construct and evaluate knowledge themselves from their own experience in a bottom-up, constructivist fashion. This position paper builds on the(More)
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