Long Short Term Memory (LSTM)-based Cuffless Continuous Blood Pressure Monitoring

Continuous blood pressure monitoring is critical for early detection and management of cardiovascular diseases. This project presents an LSTM-based cuffless continuous blood pressure monitoring system using machine learning techniques. The system employs BP and SpO₂ sensors to collect real-time physiological data, which is processed using an LSTM model to analyze temporal patterns and predict future blood pressure trends. An Arduino Mega is used as the main controller for sensor data acquisition and system coordination. The model compares present readings with predicted future values to identify abnormal conditions. In case of any critical variation, alerts are generated and transmitted to caregivers through a GSM module, along with location details obtained using a GPS module. The proposed system enables non-invasive, continuous, and intelligent health monitoring, providing timely alerts and improving patient safety, especially for remote and elderly care applications.