Design of system for parkinson's hand tremor evaluating based on machine learning

About 70% of Parkinson's disease patients have the initial symptoms of tremors at the end of upper limbs in the clinic, which seriously affects the normal work and life of patients. The severity of Parkinson's disease patients is evaluated clinically by doctors based on their experience, lacking objective evaluation criteria. It is particularly important to study an objective and fast tremor assessment method to assist doctors in the diagnosis and treatment of Parkinson's disease. In this paper, a recognition system of Parkinson's patients' hand function tremor based on machine learning is designed. Firstly, the acceleration sensor is used to collect the hand tremor signal, and then the median and band-pass filters are used to remove the noise. Next, the time-domain and frequency-domain characteristics of the tremors signal are extracted. Finally, BP neural network algorithm is used to classify the tremor degree into three categories. 12 volunteers were selected to carry out the system function experiment, and the results show that the system can achieve the classification of hand tremors, with an accuracy rate of 84.5%. The Parkinson's patient's hand tremor evaluation system designed in this paper has the advantages of low cost, small size, comfortable wearing, and high accuracy. It can assist clinical rehabilitation training and help doctors formulate scientific and reasonable rehabilitation training programs.