Untethered human motion recognition for a multimodal interface

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Ko, Teresa H., 1980-

Advisor dc:contributor.advisor

• Trevor Darrell.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/29674

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29674