Design And Implementation Of Parkinson’s Tremor Monitoring And Rating System Based On BMWFLC

As society ages,more and more people are suffering from Parkinson’s disease.Parkinson’s disease is a degenerative disorder of the nervous system characterized by tremors,stiffness,slowness and unsteady posture that affect the patient’s daily life and social activities.At present,the evaluation of the severity of Parkinson’s disease mainly relies on the semi-subjective assessment by doctors using the Unified Parkinson’s Disease Rating Scale modified by the Movement Disorder Society,and requires the assistance of doctors,so this assessment method has a certain degree of subjectivity and limitations.Therefore,this article studies the objective rating method of Parkinson’s disease,monitors and measures the severity of Parkinson’s disease in an objective way,so as to take timely measures to prevent,treat and manage Parkinson’s disease.The main research contents of this paper are:(1)In view of the problem that the existing inertial data acquisition equipment will affect the patient’s movement,this paper developed a low-power miniaturized Parkinson’s tremor monitoring and acquisition equipment,and based on this equipment,a Parkinson’s patient tremor data set was constructed.The device consists of three parts:a tremor monitoring and collection hardware terminal,an Android client,and a comprehensive management platform for Parkinson’s tremor data.The tremor monitoring and acquisition device collects tremor acceleration data through a 3-axis acceleration sensor,and transmits the data to an Android client through a Bluetooth module.The Android client not only stores and visualizes data,but also manages user data and communicates with the Parkinson’s tremor data comprehensive management platform.The main task of Parkinson’s Tremor Data Integrated Management Platform is to store and manage tremor datasets.After the tremor monitoring and collection equipment was built,this paper cooperated with the hospital to collect relevant tremor data.(2)For the tremor signal superimposed on the patient’s voluntary movement signal and pathological tremor signal,this paper proposes an improved algorithm for the separation of voluntary movement and pathological tremor based on a band-limited multi-weighted Fourier linear combiner.The voluntary motion in the data is estimated and separated by a band-limited multi-weighted Fourier linear combiner to obtain pure pathological tremor data.Finally,feature extraction and feature selection are performed on the processed data set through Hilbert-Huang transform and Relief F algorithm.(3)In order to better assist clinical diagnosis,this paper designed a Parkinson’s tremor rating system based on a multilayer perceptron,and compared the application effects of three other traditional machine learning algorithms and three deep learning algorithms horizontally,and found that the multilayer The perceptron model performs best on the Tremor dataset.After experiments,the weighted average sensitivity of the rating system is 97.0%,the weighted average specificity is 99.7%,and the weighted average F1 value is 97.0%.The experimental results show that the system proposed in this paper can objectively reflect the severity of Parkinson’s tremor in patients,which provides an important basis for doctors’ follow-up judgment and treatment.The advantage of this paper is that it uses an objective measurement method,which avoids the uncertainty of subjective evaluation.At the same time,the use of miniaturized tremor monitoring and collection equipment also greatly improves the efficiency and accuracy of data collection.In addition,a band-limited multi-weighted Fourier linear combiner is used for preprocessing,which effectively reduces the noise and interference of the data,and improves the accuracy and reliability of feature extraction.The final evaluation results have also undergone rigorous statistical analysis,proving their stability and reliability.Therefore,the system is expected to be widely used in clinical practice to provide more accurate assessment and treatment options for patients with Parkinson’s disease.