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

Helinyi Peng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki

TL;DR

It is suggested 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.

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.

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

TL;DR

It is suggested 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.

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.
Paper Structure (56 sections, 3 equations, 11 figures, 5 tables)

This paper contains 56 sections, 3 equations, 11 figures, 5 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Game-based Approaches for Emergency Preparedness and AED Engagement
  4. Behavior Tracking Technologies
  5. MOTIVATIONS AND RESEARCH QUESTIONS
  6. Motivations
  7. Research Questions
  8. Definition of Retrieval Efficiency
  9. Objective Metrics
  10. Relative retrieval-duration reduction $\boldsymbol{(\Delta D_T)}$.
  11. Relative pause-duration reduction (learning signal) $\boldsymbol{(\Delta D_P)}$.
  12. Subjective Metrics
  13. SYSTEM DESIGN: AEDHUNTER
  14. Design Principles
  15. Intended Use and Deployment Scenarios
  16. ...and 41 more sections

Figures (11)

  • Figure 1: AEDHunter Study Framework.
  • Figure 2: Screenshots of the AED hunting exam process. The process begins with the initial screen (a), where the participant's profile and study information are displayed. After selecting the hunting mode, the participant proceeds to the approaching screen (b), which shows the distance to the designated starting point on a map. Once within the required radius, the ready-to-start screen (c) confirms that the participant is correctly positioned. A countdown (d) then initiates the AED hunting session, after which the participant finds the target AED (e) by approaching the Bluetooth beacon and verifying proximity for a set duration.
  • Figure 3: Screenshots of the routine game sessions. The routine game session begins with the main screen (a), where the participant can initiate a hunting session. After starting, the simulation screen (b) displays a scenario prompting the participant to find an AED immediately. This is followed by a countdown screen (c) that indicates the imminent start of the search. During the game screen (d), the participant is guided to approach the AED location, with indicators such as signal proximity and elapsed time. Upon successfully locating the AED, the verification screen (e) confirms the completion of the session and prompts the participant to proceed to a post-session survey.
  • Figure 4: Experimental Environment. (a) and (b) show maps of the two campuses, indicating AED locations (shown as hearts icons), their floor designations (e.g., "AED 3 (2F)" for AED 3 on the second floor), the exam‑session starting point (blue flag labeled "Exam Start"), and pedestrian pathways (light grey lines). (c) A typical AED unit used in this study. (d) A Bluetooth beacon placed near each AED for arrival detection (each beacon measures under 3.5 cm).
  • Figure 5: Representative acceleration and angular velocity traces. Shaded (grey) regions denote segments labeled as exploratory pausing; unshaded regions correspond to segments labeled as moving.
  • ...and 6 more figures