Untethered human motion recognition for a multimodal interface
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2006
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| Online Access: | http://hdl.handle.net/1721.1/29674 |
| _version_ | 1826197519572402176 |
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| author | Ko, Teresa H., 1980- |
| author2 | Trevor Darrell. |
| author_facet | Trevor Darrell. Ko, Teresa H., 1980- |
| author_sort | Ko, Teresa H., 1980- |
| collection | MIT |
| description | Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003. |
| first_indexed | 2024-09-23T10:48:54Z |
| format | Thesis |
| id | mit-1721.1/29674 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:48:54Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/296742019-04-11T12:36:46Z Untethered human motion recognition for a multimodal interface Ko, Teresa H., 1980- Trevor Darrell. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2006-03-24T16:14:04Z 2006-03-24T16:14:04Z 2003 2003 Thesis http://hdl.handle.net/1721.1/29674 53833690 eng 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 58 p. 2053608 bytes 2053416 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ko, Teresa H., 1980- Untethered human motion recognition for a multimodal interface |
| title | Untethered human motion recognition for a multimodal interface |
| title_full | Untethered human motion recognition for a multimodal interface |
| title_fullStr | Untethered human motion recognition for a multimodal interface |
| title_full_unstemmed | Untethered human motion recognition for a multimodal interface |
| title_short | Untethered human motion recognition for a multimodal interface |
| title_sort | untethered human motion recognition for a multimodal interface |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/29674 |
| work_keys_str_mv | AT koteresah1980 untetheredhumanmotionrecognitionforamultimodalinterface |