IoT Enabled Health Monitoring System using Machine Learning Algorithm

Now-a-davs Internet of Things (IoT) is used in various real-time applications, Including smart health monitoring. The existing health monitoring system can only collect the basic information about heat. heartbeat. and BP (Blood Pressure). This research study proposes an effective examination of patient's brain signals and detect the health status of the patient in real time. The main objective of the proposed study is to provide a proper optimized value about the mentally challenged patients by collecting the data information from brain signals with 24 channels and study the body parameters through each EEG (Electroencephalography) signal channel. Here, the collected data is pre-processed by using Machine Learninz (ML) tools and Neural Networks (NN) with Python programming language, By collecting the data information from brain signals with EEG sensors, an optimized value and solution can be provided to the patients suffering from Cerebral Palsy (CP). The collected data is then stored in a cloud storage platform and it can be accessed from any remote location. The stored data is then collected and filtered by using PCA techniques and further the Artifact siznals (Noise) are removed to diagnose seizures by Identifying brain signal parameters (Alpha, Beta, Delta and Theta). Further, a novel model has been designed by using python programming languaze for training the machine with a maximum number of datasets in order to check accuracy and predict the seizure levels of any CP patient. Neural Network (NN) algorithms were applied here by using python programming language in order to check the percentage error in the data processing mechanism. Once the data is analyzed with the proposed model it suggests the CP patient for Tentative Treatment.