An AI-Driven Trainee Performance Evaluation in XR-Based CPR Training System for Enhancing Personalized Proficiency

Cardiac arrest is a life-threatening emergency requiring immediate intervention, with bystander-initiated Cardiopulmonary resuscitation (CPR) being critical for survival, especially in out-of-hospital situations where medical help is often delayed. Given that over 70% of out-of-hospital cases occur in private residences, there is a growing imperative to provide widespread CPR training to the public. However, conventional instructor-led CPR training faces inherent limitations regarding spatiotemporal constraints and the lack of personalized feedback. To address these issues, this paper proposes an AI-integrated XR-based CPR training system designed as an advanced auxiliary tool for skill acquisition. The system integrates vision-based pose estimation with multimodal sensor data to assess the trainee’s posture and compression metrics in accordance with Korean regional CPR guidelines. Moreover, it utilizes a Large Language Model to evaluate verbal protocols, including requesting an emergency call that aligns with the guidelines. Experimental validation of the proof-of-concept reveals a verbal evaluation accuracy of 88% and a speech recognition accuracy of approximately 95%. Furthermore, the optimized concurrent architecture provides a real-time response latency under 0.5 s, and the automated marker-based tracking ensures precise spatial registration without manual calibration. These results confirm the technical feasibility of the system as a complementary solution for basic life support education.