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http://hdl.handle.net/11375/14462
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DC Field | Value | Language |
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dc.contributor.author | Chrapka, Philip | en_US |
dc.date.accessioned | 2014-06-18T18:13:24Z | - |
dc.date.available | 2014-06-18T18:13:24Z | - |
dc.date.created | 2011-02-18 | en_US |
dc.date.issued | 2010-04-23 | en_US |
dc.identifier.other | ee4bi6/59 | en_US |
dc.identifier.other | 1034 | en_US |
dc.identifier.other | 1796598 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/14462 | - |
dc.description.abstract | <p>Since the 1970s, electromyographic control of a prosthetic device has been attempted in a number of different ways. It was only until recently, that the classification of electromyo- graphic signals was possible through the use of neural networks. With the advent of more advanced techniques like support vector machines this type of control is becoming more re- alistic. This would initiate a great step in the development of prosthetic devices. It would become possible for more advanced control of these devices with a more natural interface. This project focuses on the development of an electromyographic classification system on an embedded platform for the specific use of controlling a prosthetic device.</p> | en_US |
dc.subject | Electromyographic control | en_US |
dc.subject | support vector machines | en_US |
dc.subject | neural networks | en_US |
dc.subject | cus- tom hardware design | en_US |
dc.subject | Biomedical | en_US |
dc.subject | Electrical and Computer Engineering | en_US |
dc.subject | Biomedical | en_US |
dc.title | EMG Controlled Hand Prosthesis: EMG Classification System | en_US |
dc.type | capstone | en_US |
Appears in Collections: | EE 4BI6 Electrical Engineering Biomedical Capstones |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 757.6 kB | Adobe PDF | View/Open |
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