Classification of finger gestures from myoelectric signals
Name
46824401-MIT.pdf
Description
Full printable version
Size
3.3 MB
Format
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Checksum (MD5)
2236bffadc65dcc75f0eb5bc42241737
Author(s)
Ju, Peter M. (Peter Ming-Wei), 1977-
Advisor(s)
Leslie P. Kaelbling.
Date Issued
2000
Publisher
Massachusetts Institute of Technology
Abstract
Electromyographic signals may provide an important new class of user interface for consumer electronics. In order to make such interfaces effective, it will be crucial to map EMG signals to user gestures in real time. The mapping from signals to gestures will vary from user to user, so it must be acquired adaptively. In this thesis, I describe and compare three methods for static classification of EMG signals. I then go on to explore methods for adapting the classifiers over time and for sequential analysis of the gesture stream by combining the static classification algorithm with a hidden Markov model. I conclude with an evaluation of the combined model on an unsegmented stream of gestures.
Description
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 73-75).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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