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This paper aims to develop a technique for repetitive motion detection which is necessary for human behavior analysis particularly in children with autism spectrum disorders. Images from video sequences are mainly investigated. The technique uses image self-similarity measure, which is less sensitive to view changes, noise, and stable to low resolution images, as input data to multilayer perceptron neural network. Outputs of the network are composed of two classes, which are repetitive and non-repetitive motions. The classifier uses training data from a single person. The model is created by 10 fold cross validation. Trained network is tested with different data sets from seven normal subjects. The classification results show that the proposed technique provides an average accuracy of 0.9115 and can be used in real-time manner. In addition, the trained classifier is robust to images taken from different view.Keywords-repetitive motion detection; image self similarity measure; neural networks; children with autistic spectrum disorder.

Animal-Assisted Therapy (AAT) is the science that employs the merit of human-animal interaction to alleviate mental and physical problems of persons with disabilities. However, to achieve the goal of AAT for persons with severe disabilities (e.g. spinal cord injury and amyotrophic lateral sclerosis), real-time animal language interpretation is needed. Since canine behaviors can be visually distinguished from its tail, this paper proposes the automatic real-time interpretation of canine tail language for human-canine interaction in the case of persons with severe disabilities. Canine tail language is captured via two 3-axis accelerometers. Directions and frequency are selected as our features of interests. New fuzzy rules and center of gravity (COG)-based defuzzification method are proposed in order to interpret the features into three canine emotional behaviors, i.e., agitate, happy, and scare as well as its blended emotional behaviors. The emotional behavior model is performed in the simulated dog. The average recognition rate in real dog is 93.75% accuracy.

This paper aims to investigate missing data techniques for effective prediction of nasopharyngeal carcinoma (NPC) recurrence. The techniques include listwise deletion, imputations by mean, k-nearest neighbor, and expectation maximization. The completed data are used to predict the presence or absence of NPC recurrence in each year by means of logistic regression, multilayer perceptron with backpropagation training, and naïve bayes. Five year predictions are carried out. Validity of each predictive model is assured by 10-fold cross validation. Their results are compared in order to determine proper missing data treatment and the most efficient prediction technique. The results showed that EM imputation was superior to the other missing data techniques because it can be efficiently applied to all predictive models. The multilayer perceptron with backpropagation training gave the highest prediction performance and it was the most robust to the data completed by different missing data techniques.

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