We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as\nhand tremors and nonvoluntary movements. These involuntary motor features are closely related to the symptoms occurring in\npatients suffering from Huntington�s disease (HD). We propose a hybrid (neurofuzzy) model that combines an artificial neural\nnetwork (ANN) to predict the functional capacity level (FCL) of a person and a fuzzy logic system (FLS) to determine a stage of\nreaction. We analyzed our own dataset of 3032 records collected from 20 test subjects (both healthy and HD patients) using\nsmart phones or tablets by asking a patient to locate circular objects on the device�s screen. We describe the preparation and\nlabelling of data for the neural network, selection of training algorithms, modelling of the fuzzy logic controller, and\nconstruction and implementation of the hybrid model. The feed-forward backpropagation (FFBP) neural network achieved the\nregression R value of 0.98 and mean squared error (MSE) values of 0.08, while the FLS provides a final evaluation of subject�s\nreaction condition in terms of FCL.
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