Wearable Parkinson's Disease Finger Tapping Quantitative Evaluation Algorithm Combined with Impedance Sensing

This paper proposes an Artificial Intelligence (AI) identification algorithm that combined the human body resistance and capacitance sensing. The measured human body impedance data is analyzed by a simple four-arithmetic algorithm, and then four different AI algorithms are used to determine whether or not according to the characteristics of Parkinson's Disease (PD) patients. The algorithm of this paper is based on the impedance data of normal people and PD patients through the calculation circuit proposed in this paper to analyze the difference in body resistance, the number of finger fits, finger kneading cycles, and finger kneading amplitude to accurately distinguish the fingers of PD patients Symptoms of tremor and stiffness. Through the feature analysis of four AI algorithms, it is judged that the accuracy rate of PD patients is higher than 90%.