One of the most frequently diagnosed neurodegenerative disorders, along with Alzheimer's disease, is Parkinson's disease. It is a slowly progressing disease of the central nervous system that affects parts of the brain which are responsible for one's motor functions. Despite the frequency of its occurrence among the elderly population, there has not yet been established a universal approach towards its certain diagnostics ante mortem. The study presents a pilot experiment regarding the assessment of the usefulness of simultaneous processing and analysis of speech signal and hand tremor accelerations for patient's screening and monitoring of the progress in healing, using the data acquired with a mid-range Android smartphone. During the study, a mobile device of this kind was used to record the patients of the Department of Neurology, University Hospital of the Jagiellonian University in Kraków and a control group of healthy persons over the age of 50. The samples were then analysed and an attempt towards classification was made using statistical methods and machine learning techniques (PCA, SVM, LDA). It was shown that even for a limited population, the classifier reaches about 85% accuracy. Another topic discussed in the study is the possibility of implementing a fully automated mobile system for the monitoring of the disease's progression. Propositions of further research were also drawn.
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