Untethered human motion recognition for a multimodal interface
Name
53833690-MIT.pdf
Description
Full printable version
Size
3.73 MB
Format
Adobe PDF
Checksum (MD5)
37f6874a9e7555086bed2cfda372ebaa
Author(s)
Ko, Teresa H., 1980-
Advisor(s)
Trevor Darrell.
Date Issued
2003
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 55-58).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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