Automatic Detection of Physiological Attributes from Verbal Communication During Time-Critical Medical Events

In this study, we present a proof-of-concept system exploring the feasibility of automatically capturing physiological values during the primary survey phase of trauma resuscitation—a complex, time-sensitive evaluation of severely injured patients. We developed and evaluated a speech-driven activity recognition system designed to detect and extract key physiological parameters (e.g., age, manual blood pressure, Glasgow Coma Scale) from spoken clinical dialogue in real-time. By using audio recordings from actual trauma resuscitation cases, our system performs streaming inference to identify verbalized measurements associated with primary survey activities. Experimental results show that our approach can recognize seven attributes with an average accuracy of 72% in a challenging clinical environment with acceptable latency. We present this work as an initial feasibility demonstration rather than a deployment-ready system, highlighting both the opportunities and the remaining challenges of automating documentation in fast-paced emergency environments.