AEDHunter: Investigating AED Retrieval in the Real World via Gamified Mobile Interaction and Sensing

2026-03-01

AI Summary

This study addresses the critical gap in public awareness of automated external defibrillator (AED) locations, which often delays early defibrillation during out-of-hospital cardiac arrest. To tackle this issue, the authors propose AEDHunter—a location-based mobile application that uniquely integrates gamification with real-world AED search training, guiding users to repeatedly practice locating AEDs in authentic environments. The system leverages smartphone sensors and low-energy Bluetooth tags to log user behavior and introduces “exploratory pauses” as a novel metric to quantify learning progression, capturing the dynamic reduction in user hesitation over time. Experimental results demonstrate that repeated training significantly reduces search time and markedly improves users’ self-reported confidence in AED localization, thereby validating the efficacy of gamified in-situ training for enhancing public readiness in emergency response.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Early defibrillation significantly improves survival rates in cases of out-of-hospital cardiac arrest. However, limited public awareness of Automated External Defibrillator (AED) locations constrains their effective use. Existing solutions, such as static 2D maps, often fall short in urgent or complex real-world scenarios. To address this challenge, we developed AEDHunter, a gamified, location-based mobile application designed to transform AED retrieval into an engaging and repeatable practice experience. Leveraging smartphone sensors to analyze participants' movement and learning patterns, and using low-cost Bluetooth tags to verify arrivals at AED locations, AEDHunter guides users through multiple sessions of AED discovery. In a real-world evaluation study, participants significantly reduced their AED retrieval times after repeated practice sessions and reported increased confidence in locating AEDs. Additionally, we employ a two-state activity detector to identify ``exploratory pauses'', which are then used as a behavioral learning signal to quantify hesitation and its progressive reduction through practice. Our findings suggest that gamified applications like AEDHunter can improve AED retrieval performance through repeated, in-situ training and enhance self-reported preparedness, offering design insights for technology-supported learning and public safety applications.

Problem

• AED retrieval
• public awareness
• out-of-hospital cardiac arrest
• emergency response
• location-based training

Innovation

• gamified mobile interaction
• smartphone sensing
• Bluetooth beacon verification
• exploratory pause detection
• in-situ AED training