Toward autism recognition using hidden Markov models

by Lancaster, Joseph Paul

Abstract (Summary)
The use of hidden Markov models in autism recognition and analysis is investigated.

More speci?cally, 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 ?rst 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.

Bibliographical Information:


School:Kansas State University

School Location:USA - Kansas

Source Type:Master's Thesis

Keywords:hidden markov model autism computer science 0984 psychology clinical 0622 developmental 0620


Date of Publication:01/01/2008

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