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)
Myoelectric training is a key step in transitioning upper limb amputee patients to a successful myoelectric prosthesis fitting. Although training was found to be important in fittings with children [1], a gap in the literature exists for linking training systems to improved outcomes in adults. In order to close this gap additional studies must be performed(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)
Predictions are a key component to intelligence and necessary for accurate motor control. In reinforcement learning, such predictions can be made through general value functions (GVFs). This paper introduces prosthetic arms as a domain for artificial intelligence and discusses the role that predictions play in prosthetic limb control. We explore the use of(More)
Advanced neuroprosthetic devices demonstrate an impressive capacity for both actuation and sensation, providing numerous controllable degrees of freedom and reportable sensory percepts. When linked to the human body by way of invasive and non-invasive brain-body-machine interfaces, neuroprostheses promise to greatly improve life for users by extending their(More)
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