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EMG-based Robot Control Interfaces: Beyond Decoding


Abstract Electromyogram (EMG)-based control interfaces are increasingly used in robot teleoperation, prosthetic devices control and also in controlling robotic exoskeletons. Over the last two decades researchers have come up with a plethora of decoding functions to map myoelectric signals to robot motions. However, this requires a lot of training and validation data sets, while the parameters of the decoding function are specific for each subject. In this thesis we propose a new methodology that doesn't require training and is not user-specific. The main idea is to supplement the decoding functional error with the human ability to learn inverse model of an arbitrary mapping function. We have shown that the subjects gradually learned the control ... (more)
Created Date 2013
Contributor Antuvan, Chris Wilson (Author) / Artemiadis, Panagiotis (Advisor) / Muthuswamy, Jitendran (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Subject Mechanical engineering / Robotics
Type Masters Thesis
Extent 88 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Mechanical Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis