AEDHunter: Investigating AED Retrieval in the Real World via Gamified Mobile Interaction and Sensing
Peng, Helinyi, Taya, Akihito, Nishiyama, Yuuki, Sezaki, Kaoru
In: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 10 (19), pp. 1 - 30 , 2026.
@article{imwut2026peng,
author = {Peng, Helinyi and Taya, Akihito and Nishiyama, Yuuki and Sezaki, Kaoru},
title = {AEDHunter: Investigating AED Retrieval in the Real World via Gamified Mobile Interaction and Sensing},
year = {2026},
issue_date = {March 2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {10},
number = {1},
url = {https://doi.org/10.1145/3789689},
doi = {10.1145/3789689},
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.},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
month = mar,
articleno = {19},
numpages = {30},
keywords = {Out-of-Hospital Cardiac Arrest, Gamified Mobile Sensing, AED Retrieval, Behavioral Learning}
}
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.