Implementation of a NEWS 2-Based Early Warning System for Multiparameter Vital Sign Monitoring
DOI:
https://doi.org/10.47709/brilliance.v6i4.9884Keywords:
NEWS2, vital signs, ESP32, Internet of Things, early warning systemAbstract
Integrated vital-sign monitoring can support earlier recognition of physiological deterioration when measurements are converted into a consistent track-and-trigger score. This study designed and implemented an Internet of Things (IoT)-based multiparameter monitoring prototype that calculates the National Early Warning Score 2 (NEWS2). A MAX30102 measured peripheral oxygen saturation (SpO?), two MLX90614 sensors measured body temperature and respiratory rate, and an OMRON HEM-7156T supplied systolic/diastolic blood pressure and pulse rate to an ESP32 through Bluetooth Low Energy. The ESP32 transmitted data over Wi-Fi and MQTT to a Node.js backend, PostgreSQL/TimescaleDB storage, and a React dashboard; NEWS2 results were returned to a 20×4 LCD, LEDs, and a buzzer. Thirty paired measurements were used for sensor comparison, while the complete system was evaluated on 31 participants aged 20–81 years. The mean absolute differences were 0.26 percentage points for SpO?, 0.07 °C for temperature, and 0.40 breaths/min for respiratory rate. Corresponding MAPE values were 0.26%, 0.19%, and 2.58%. The 31-participant dataset produced mean values of 14.77 breaths/min, 97.99% SpO?, 84.77 bpm, 37.36 °C, and 121.65 mmHg systolic pressure. Total NEWS2 scores ranged from 0 to 4, and all participants were categorized as low risk without a single red score. Simulated scores of 6 and 9 verified medium-risk, high-risk, and single-red-score alert branches. The prototype demonstrated end-to-end technical feasibility, but the reported agreement and risk outputs are not a clinical validation and require calibrated references, repeated measurements, broader cohorts, and formal clinical evaluation
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