2026-09-20T12:29:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1216292026-06-06T00:48:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Ognjen Rudovic and Rosalind Picard.
Liu, Amanda Jin.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-15T20:29:21Z
2019-07-15T20:29:21Z
2018
2018
https://hdl.handle.net/1721.1/121629
1098173981
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 47-49).
When building personalized models of affect, hierarchical structures are important in creating levels of separation and sharing between models. Past studies have indicated that semantic hierarchies along demographic divisions perform well in estimating affect. This work focuses on comparing these semantic groupings to data-driven hierarchies. A key question is whether data-driven hierarchies can provide additional ways of understanding affect, outside of semantic boundaries. The experiments are conducted in the context of therapy sessions between personal robots and children with autism. The results reveal novel data-driven hierarchies that could grant better understanding of autism and facilitate more versatile interactions between child and robot.
by Amanda Jin Liu.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
49 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Semantic and data-driven hierarchies for personalized models of affect
Thesis
application/pdf
2019-07-15T20:29:19Z
Publication
Master
EECS
unknown
unknown
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