Advancing Pre-Hospital Emergency Medical Services: An AI-Powered Approach to Voice-Activated Technologies

• Published in: 2024 IEEE 14TH SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS, ISCAIE 2024
• Main Authors: Azman, Nur Atiqah; Abdullah, Samihah; Hadis, Nor Shahanim Mohamad; Faiza, Zafirah; Hamid, Shabinar Abdul; Azmin, Azwati
• Format: Proceedings Paper
• Language: English
• Published: IEEE 2024
• Subjects: Computer Science; Engineering
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001283898700075

The introduction of Artificial Intelligence (AI) voice recognition technology, particularly Automatic Speech Recognition (ASR), represents a significant advancement in human-computer interaction. In the context of emergency medical services (EMS), which oversees pre-hospital patient care, this project aims to address challenges faced by paramedics, particularly those with occupied hands, in efficiently recording multiple vital sign parameters through a voice command system. Leveraging the Internet of Things (IoT) and wireless body area networks, the project focuses on creating a simple yet effective device for wirelessly transmitting vital sign data such as body temperature, pulse rate, and oxygen saturation to a remote monitoring platform. The precision and timeliness of contextual information are crucial in emergency care, and the project aims to enhance data accuracy during emergency transportation and calls through the integration of Voice Recognition (VR) Module V3, MAX30100, LM35, Arduino Mega, ESP32, and the ThingsBoard platform. By developing a VR system, the project seeks to contribute valuable insights to improve pre-hospital emergency medical services and offer recommendations for strengthening the overall pre-EMS infrastructure. Successful implementation requires expertise in IoT, communication protocols, and the ThingsBoard platform. Additionally, the project quantitatively evaluated the performance of the VR system, demonstrating a significant improvement in data accuracy and timeliness compared to traditional manual recording methods. The data gathered from the vital sign sensors has been successfully transmitted to ThingsBoard in real-time, with a high degree of reliability and efficiency, further validating the effectiveness of the proposed solution.