Master of Science / Department of Computing and Information Sciences / David A. Gustafson / The use of hidden Markov models in autism recognition and analysis is investigated.
More specifically, we would like to be able to determine a person's level of autism (AS, HFA,
MFA, LFA) using hidden Markov models trained on observations taken from a subject's
behavior in an experiment. A preliminary model is described that includes the three mental
states self-absorbed, attentive, and join-attentive. Futhermore, observations are included
that are more or less indicative of each of these states. Two experiments are described,
the first on a single subject and the second on two subjects. Data was collected from one
individual in the second experiment and observations were prepared for input to hidden
Markov models and the resulting hidden Markov models were studied. Several questions
subsequently arose and tests, written in Java using the JaHMM hidden Markov model tool-
kit, were conducted to learn more about the hidden Markov models being used as autism
recognizers and the training algorithms being used to train them. The tests are described
along with the corresponding results and implications. Finally, suggestions are made for
future work. It turns out that we aren't yet able to produce hidden Markov models that are
indicative of a persons level of autism and the problems encountered are discussed and the
suggested future work is intended to further investigate the use of hidden Markov models
in autism recognition.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/777 |
| Date | January 1900 |
| Creators | Lancaster, Joseph Paul Jr. |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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