A Clinically Informed ML-IEMS Architecture for Wearable Vital-sign Monitoring with Retrospective Simulation

This study proposes a clinically informed, energy-aware Internet of Things (IoT) architecture for wearable vital-sign monitoring, addressing literature-reported limitations and healthcare-professional requirements. A sequential methodology was adopted. A bibliometric analysis of 310 Scopus-indexed articles published between 2019 and 2024 identified frequently monitored physiological parameters, sensing approaches, and recurrent system limitations. A survey of 76 healthcare professionals captured expectations regarding real-time monitoring, alerts, usability, data access, security, and system integration. These findings were translated into a three-tier wearable IoT architecture incorporating National Early Warning Score 2 (NEWS2)-based risk assessment and a machine-learning-assisted Intelligent Energy Management System (ML-IEMS). Retrospective validation used an initial cohort of 1500 VitalDB cases, with final policy-level simulation on 276 independent test cases totaling 1058 h and comparison against always-on monitoring and a rule-based IEMS baseline (Rule-IEMS). Heart rate, respiratory rate, and blood pressure were the most investigated parameters, while key gaps concerned autonomy, motion robustness, validation size, and multi-parameter integration. The survey confirmed demand for multi-parameter sensing, energy autonomy, and real-time data access. In simulation, estimated battery autonomy increased from 71 h with always-on monitoring to 155 h with Rule-IEMS and 159 h with ML-IEMS. ML-IEMS preserved episode sensitivity of 1.000 for NEWS2 ≥ 5 events, with a false-alarm burden of 1.06 alerts/h, indicating that future work should further optimize alert specificity. The proposed architecture links literature-derived limitations and healthcare-professional needs to a simulation-supported ML-IEMS policy, improving autonomy and signal fidelity while maintaining clinical sensitivity.