DT-GRU Approach for Developing Remotely Monitored Central Nervous System Using Wearable Sensors

The capacity to evaluate numerous bio- behavioral factors concurrently is a potential strength of RMT for patients with neurological diseases. Although the benefits of RMT have been demonstrated in a large body of research, there is still some debate over its usefulness among medical experts. Providing doctors with visual summaries of data in the patient record is one way that RMT could enhance the system of care for patients with illnesses of the central nervous system. It's important to think about the constraints on the benefits these technologies can provide and the difficulties of using them effectively. The proposed strategy makes use of pre-processing, feature selection, and model training. Data is preprocessed in LMS. Feature selection employs PCA. The DT -GRU algorithm is used to train the model. Two additional models, GRU and DT, are contrasted with the suggested method. The proposed method is quite effective.