2026-09-19T02:06:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/296742022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Trevor Darrell.
Ko, Teresa H., 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-03-24T16:14:04Z
2006-03-24T16:14:04Z
2003
2003
http://hdl.handle.net/1721.1/29674
53833690
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 55-58).
This thesis used machine learning techniques to extract useful information about human body articulations. First, it presents a learning approach to model non-linear constraints; a support vector classifier is trained from motion capture data to model the boundary of the space of valid poses. Next, it proposes a system that incorporates body tracking and gesture recognition for an untethered human-computer interface. The detection step utilizes an SVM to identify periods of gesture activity. The classification step uses gesture-specific Hidden Markov Models (HMMs) to determine which gesture was performed at any time period, and to extract the parameters of those gestures. Several experiments were performed to verify the effectiveness of these techniques with encouraging results.
by Teresa H. Ko.
M.Eng.
58 p.
2053608 bytes
2053416 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Untethered human motion recognition for a multimodal interface
Thesis
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