A neural network system for diagnosis and assessment of tremor in parkinson disease patients

Tremor is one of the most important symptom in Parkinson's disease, which has been assessed clinically by neurologists as part of UPDRS scale. In this paper, we have implemented a supervised learning pattern recognition system to assess UPDRS of each Parkinson patient tremor to fill the absence of a reliable diagnosis and monitoring system for Parkinson patients. In our system a simple noninvasive method based on the recorded acceleration through the smartphone have been used for data acquisition. The results show high accuracy in the classifier block and neural network. A tight correlation between UPDRS scale and acceleration values reveals 91 percent accuracy by neural network with two hidden layers.