Oregon State University

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PhD Oral Preliminary Examination – Henrique Dantas


Wednesday, February 14, 2018 3:00 PM - 5:00 PM

Controlling Smart Prostheses for Complex Tasks
Movement decoders allow amputees to control prostheses in a natural way by interpreting motor bioelectrical signals, giving them the chance to perform day-to-day tasks. Such systems have to overcome a number of challenges before they can become practical. These challenges include understanding the complexity of the underlying biochemical systems, the recursive nature of the human decision making process, the limited amount of data typically available for training and the time varying properties of the nervous system. I propose to use systems trained using Markov Decision Process models to learn complex tasks using neural networks. I propose to use hybrid controllers that combine the decoded information with a higher level goal to perform tasks. Finally, I propose an adaptive framework for neural decoders base on semi-supervised learning. Once completed, the neural prostheses should feel and behave like natural arms and will enable amputees to regain the ability to perform most of the tasks that the original limbs could perform. This proposal not only has a compelling social impact, but also will contribute to the data science community.

Major Advisor: V John Mathews
Committee: Mario Magaña
Committee: Fuxin Li
Committee: Alan Fern
GCR: Julie Tucker


Kelley Engineering Center (campus map)
1007
Calvin Hughes
1 541 737 3168
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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