Nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier for automatic assessing of tremor severity

Neurological diseases like Alzheimer, epilepsy, Parkinson´s disease (PD), and other dementias influence the lives of patients, their families and society. In PD the brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson´s disease tremor analysis. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptrons - MLP) and an Adaptive Neuro-Fuzzy Classifier (ANFC). In general, the results may be expressed as a prognostic (risk degree to contact PD).