Real-Time Risk and Triage Analytics

The service processes sensor data (e.g., vital signs, environmental inputs) to quantify risks and prioritize actions, preventing damage in critical scenarios. For example, it supports astronauts by retracting suit mechanisms during anomalies, soldiers in CBRN scenarios by prioritizing evacuation, or EMA HTA trials by monitoring medication effects. Deployed on edge devices with TensorFlow Lite, it ensures offline autonomy and hybrid scalability with cloud integration. Validated in DLR’s :envihab, it reduces decision-making errors by up to 50%, enhancing safety and efficiency across sectors.