A Proposal for an Self-Rescue System Based on offline LLM for Minor Injuries after an Earthquake

Earthquakes cause several losses every year, both in terms of human lives and infrastructure damage. Therefore, it is increasingly important to think of new tools that reduce the damage caused by an earthquake as much as possible. In this context, the role of Earthquake Early Warning Systems (EEWSs) is becoming increasingly significant. These systems can rapidly detect earthquakes and issue alerts that guide emergency safety actions. They capture ground shaking shortly after an earthquake occurs and raise alarms in target areas with an advance of seconds to several tens of seconds before the strong ground motion impacts on target infrastructures. This paper proposes the design of an system intended to assist a moderately injured operator, after a seismic event within a critical environment, who is unable to communicate with emergency responders. The system provides autonomous self-rescue instructions that aim to mitigate the risk of additional complications from injuries. The system architecture is based on the integration of Large Language Models (LLMs) offline with the continuous monitoring of selected vital parameters, periodically acquired through wearable smartwatch technology.