Design and Evaluation of a Wearable IoT Triage System for Mass Casualty Management

This study presents the design and evaluation of a wearable Internet of Things (IoT)–based triage system developed to improve the prioritization of casualties during mass casualty incidents (MCIs). The main objective of the research was to enhance the speed and accuracy of patient assessment while reducing the cognitive workload of first responders. The system was designed as an integrated platform combining wearable sensor nodes, a low-power long-range communication link based on LoRa technology, and an interactive dashboard for real-time monitoring and classification. A MAX30102 photoplethysmography sensor was used for continuous measurement of heart rate and oxygen saturation, while a LoRa-enabled transmitter based on the RFM95 module sent physiological data to a central gateway built around a Raspberry Pi microcontroller. The triage decision logic followed a semi-automated adaptation of the START protocol, implemented using a rule-based flow to categorize patients based on vital sign thresholds and consciousness level. Physiological data were continuously analyzed, and the corresponding triage status was updated on a real-time interface designed to support medical staff during emergency operations. Experimental assessments conducted under controlled simulation scenarios confirmed that the proposed architecture effectively supported stable communication, timely data updates, and consistent triage decisions. Key findings indicated that the wearable system maintained high reliability, adaptability, and responsiveness in the absence of internet connectivity. Overall, the proposed approach demonstrates that IoT-enabled wearable technologies can substantially improve the operational efficiency of disaster medicine by enabling continuous patient monitoring and data-driven prioritization strategies in critical environments.