Smart Med Ambulance: AIOT-Based Real Time Patient Monitoring and Emergency Response System

In emergency medical situations, timely monitoring and rapid medical decision-making are critical for saving lives. This project presents a Smart Health Monitoring System for Ambulances, an IoT- and Machine Learning–based solution designed to continuously monitor a patient’s vital signs during transit and transmit them to healthcare professionals in real time. The system integrates biomedical sensors to measure essential parameters such as heart rate, blood pressure, SpO₂, body temperature, and ECG signals. These sensors are interfaced with an ESP32 microcontroller, which collects, processes, and wirelessly transmits the data to a cloud-based platform using Wi-Fi connectivity. A Machine Learning model analyzes the received data to assess the patient’s health condition and classifies it into categories such as stable, moderate, or critical. This real-time analysis enables doctors at the hospital to monitor the patient remotely and prepare necessary medical interventions before the ambulance arrives. The automated system reduces the workload on paramedics, minimizes human error, and improves the accuracy of emergency diagnosis. Overall, the proposed system enhances pre-hospital care by enabling continuous monitoring, early detection of critical conditions, and faster medical response. By combining IoT, cloud computing, and machine learning, this project contributes to the advancement of smart healthcare and improves patient survival rates during emergency transportation.