AI-Based Passenger Health Monitoring and Emergency Response System for Hyperloop Transportation Using Wearable Skin Patches

Hyperloop transportation systems propose to carry passengers in sealed, low-pressure capsules at speeds exceeding 1000 km/h, an environment in which sudden cabin depressurization, high g-force transitions, and prolonged sensory isolation can trigger rapid physiological deterioration with almost no time for manual intervention. This paper proposes an AI-based passenger health monitoring and emergency response system built around a wearable, adhesive skin patch. The patch continuously acquires heart rate, SpO2, ECG, skin temperature, and galvanic skin response, and processes this data using an on-patch edge-AI inference engine capable of tiered decision-making: minor, self-correctable physiological deviations are handled autonomously by the patch through haptic alerts and micro-dosed transdermal intervention, while critical anomalies such as cardiac arrhythmia, hypoxia, or seizure onset are escalated instantly to the Hyperloop pod's onboard controller and a centralized control room, triggering automated deceleration protocols, oxygen supply activation, and emergency medical alerts at the nearest pod station. The system architecture, sensor fusion methodology, tiered AI decision model, and communication protocol are presented, along with expected performance benchmarks validated through simulation.