Full metadata
Title
EMG-based robot control interfaces: beyond decoding
Description
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 strategy and their learning rates improved. We also worked on identifying an optimized control scheme that would be even more effective and easy to learn for the subjects. Optimization was done by taking into account that muscles act in synergies while performing a motion task. The low-dimensional representation of the neural activity was used to control a two-dimensional task. Results showed that in the case of reduced dimensionality mapping, the subjects were able to learn to control the device in a slower pace, however they were able to reach and retain the same level of controllability. To summarize, we were able to build an EMG-based controller for robot devices that would work for any subject, without any training or decoding function, suggesting human-embedded controllers for robotic devices.
Date Created
2013
Contributors
- Antuvan, Chris Wilson (Author)
- Artemiadis, Panagiotis (Thesis advisor)
- Muthuswamy, Jitendran (Committee member)
- Santos, Veronica J (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
ix, 78 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.17962
Statement of Responsibility
by Chris Wilson Antuvan
Description Source
Viewed on Dec. 12, 2013
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2013
bibliography
Includes bibliographical references (p. 59-61)
Field of study: Mechanical engineering
System Created
- 2013-07-12 06:25:15
System Modified
- 2021-08-30 01:41:22
- 3 years ago
Additional Formats