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) |
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| 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 |
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| 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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