Smart Healthcare Monitoring System for Early Diagnosis of Patient's Disease using Artificial Intelligence

The mental and physical health of an individual may be monitored using such a device. Many different types of illness may be traced back to stress, anxiety, and high blood pressure. Anxiety, stress, and pressure are all common among the elderly and need specialized care in this situation. Artifact identification for BP and PPG data was developed in this work and is now fully automated. It was suggested that the vital signs signal be processed in such a way that any anomalies caused by motion artefacts be removed automatically. Then, the systolic blood pressure and diastolic blood pressure connection was analyzed using eleven characteristics extracted from the oscillometric waveforms envelope. The blood pressure estimation technique suggested in this work has been shown to be accurate. The suggested design takes use of sophisticated regression to estimate the patient's diastolic and systolic levels of pressure from the parameters of the PPG signal.