A Behavior Tree Cognitive Assistant System for Emergency Medical Services

Abstract

This paper presents a cognitive assistant system for emergency medical services (EMS) that can serve as a rescue robot or virtual assistant, helping with improving situational awareness of the first responders through automated collection and analysis of data from the incident scene and providing suggestions to them. The proposed system relies on a Behavior Tree (BT) framework that combines the knowledge of EMS protocol guidelines with speech recognition, natural language processing, and machine learning methods to (i) extract critical information from responders' conversations and verbalized observations, (ii) infer the incident context, and (iii) decide on safe and effective response interventions to perform. We use a data-set of 8302 real EMS call records from an urban, high volume regional ambulance agency in the U.S. to evaluate the responsiveness and cognitive ability of the system and assess the safety of the suggestions provided to the responders. The experimental results show that the developed cognitive assistant achieves an average top-3 accuracy of 89% in selecting the correct EMS protocols and an average F1-score of 71% in suggesting the protocol specific interventions while providing transparency and evidence for the suggestions.