Python
Aim:
The mainstay of the project is To design a low-cost IoT-based emergency triage system that collects vital health parameters, computes a Critical Index, and automatically recommends the most suitable nearby hospital.
Introduction:
Access to timely and appropriate medical care remains one of the biggest challenges in emergency healthcare, especially in rural regions, crowded cities, and areas with limited ambulance coordination. Many patients experiencing cardiac distress, low oxygen saturation, or abnormal vital fluctuations often fail to reach the right hospital on time because there is no automated system that evaluates the severity of their condition and guides them to a suitable healthcare facility. This delay, even by a few minutes, can determine survival.
This project aims to bridge that gap by creating a low-cost, IoT-enabled decision support system that uses real-time vitals to assess patient urgency and automatically identify the most appropriate hospital nearby. Using an ESP32 and simple medical sensors, essential parameters such as heart rate, SpO₂, and ECG are captured at the point of need. These readings, along with basic demographic inputs like age and gender, are uploaded directly to the cloud. A local Python server processes this data, computes a Critical Index, and evaluates hospital suitability. The result is returned to the cloud and delivered to an Android app, guiding the patient or caregiver with real-time navigation.
Proposed system:
In the proposed system, the ESP32 functions as the hardware controller and collects essential physiological data such as heart rate, oxygen saturation, and ECG signals. The patient or operator also enters age, gender, and the required medical department using a keypad. Instead of sending data directly to a local server, the ESP32 uploads all collected information to the cloud database in real time.
A local Python server runs continuously and retrieves the latest patient data from the cloud. Using signal features and demographic inputs, the Python model computes a Critical Index, indicating how severe or life-threatening the patient’s condition is. Based on this index, the server identifies the most suitable nearby hospital by evaluating parameters such as distance, department availability, estimated travel time, and capability to handle the patient’s condition.
The server then uploads the selected hospital details back to the cloud, including latitude, longitude, department, and estimated travel time. The Android application fetches this processed data from the cloud and displays the patient’s vitals, the Critical Index, and a Google Maps route link to the recommended hospital.
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