Optimizing Accuracy of Seamless Data Transmission in RTOS-Based Systems for Emergency Medical Interventions

• Published in: International Seminar on Intelligent Technology and its Applications pp. 473 - 478
• Main Authors: Azmin, Azwati, Abdullah, Samihah, Faiza, Zafirah, Sallah, Siti Sarah Md, Fauzi, Najwa Rawaida Ahmad, Negara, Mohamad Agung Prawira
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.07.2025
• Subjects: Accuracy; Emergency Medical Services; Internet-ofThings; Medical devices; Motion artifacts; Operating systems; Pre-Hospital Care; Predictive analytics; Prototypes; Real Time Operating System; Real-time systems; Temperature measurement; Temperature sensors
• ISSN: 2769-5492
• DOI: 10.1109/ISITIA66279.2025.11137484

Pre-hospital care involves delivering immediate medical attention to patients at the scene of an emergency before hospital transport. This critical phase requires paramedics to assess conditions, perform interventions, and stabilize patients en route. Accurate and continuous data collection of vital signs is essential for effective decision-making; however, traditional methods relying on manual data entry are inefficient and prone to delays due to the rapidly changing nature of vital signs. This study introduces a novel real-time vital signs monitoring system leveraging an ESP32 microcontroller interfaced with AD8232 ECG, MAX30100 SpO2, and DS18B20 temperature sensors. This proposed system uniquely integrates Blynk for instant alerts and Google Sheets for trend analysis - ensuring both immediate and retrospective patient monitoring. The system was tested on 12 subjects during rest and post-exercise states and benchmarked against standard medical devices. Results showed a strong correlation: body temperature (95.92% rest, 95.95% post-exercise), \text{SpO}_{2} (98.97% rest, 98.98% postexercise). The prototype maintained acceptable percentage errors: temperature (1.21%), \text{SpO}_{\mathbf{2}} (0.98%), and heart rate (3.47%). These minor deviations are attributed to motion artifacts, post-activity physiological variability, and sensor placement during measurement. The findings validate the reliability of this low-cost, scalable solution for enhancing emergency response efficiency. Future improvements will focus on dynamic signal filtering, predictive analytics, and wider deployment scenarios using digital twin technology, diverse population to enhance robustness and reliability.