Deeply Trained Real-Time Body Sensor Networks for Analyzing the Symptoms of Parkinson's Disease

Parkinson's Disease (PD) is a neural system disorder that disturbs the mental activities and physical activities of human beings. Analyzing the symptoms and biosignal data of PD is crucially focused in medical research fields. The existing PD diagnosis models are limited to real-time issues, insufficient deep data extraction, and early monitoring problems. On the scope, the proposed Optimal Health Support and PD Analysis System (OHPAS) analyses the symptoms of PD using a deeply trained biosensors network environment. The novel system trains the biosensor network using complex Machine Learning (ML) and Deep Learning (DL) approaches. The environment of OHPAS sets up acoustic sensors (UT-PF), microphones (MC-1500 unit), and multimodal sensor units (MC-10 sensor). MC-10 is the sensor suite that has an accelerometer sensor, gyro sensor, and Electro Cardio Gram (ECG) sensor to observe the biosignals. For establishing the biodata analysis framework, OHPAS initiates the fusion of Variable Auto Encoder (VAER) and K-Means clustering techniques. This phase comprises dataset feature reduction, data regularization, and clustering operations to make the dataset effective for the training process. Finally, the Long Short Term Memory network (LSTM) uses the preprocessed dataset for computing the training dataset. The proposed OHPAS contributes novel features such as a real-time patient monitoring environment, effective sensor data reduction, distributed sensor data analysis, day-wise PD symptom prediction, reactive PD alerts, and accurate early detection solutions. Considering effective medical data analysis with minimal response time, the proposed model creates reactive body sensor network. Under this sensor platform, sensor modules contain proposed DL procedures in its internal memory for initiating data analysis practices. Consequently, the symptoms of PD are commendably detected and predicted with minimal response time. The experimental results indicate the proposed PD system outperforms the existing systems with 8% to 10% of better results.