Toward autism recognition using hidden Markov models

Abstract

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.