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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

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.

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    Date Created
    • 2013
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  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2013
      Note type
      thesis
    • Includes bibliographical references (p. 59-61)
      Note type
      bibliography
    • Field of study: Mechanical engineering

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    by Chris Wilson Antuvan

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