@inproceedings{bc7d5002836f43939e37827bf6e4a497,
title = "HEALSound: Healthcare Education And Labeling for Cardiopulmonary Sounds",
abstract = "Advances in digital stethoscopes and wearable health sensors now support real-time cardiopulmonary monitoring and AI-driven diagnostic tools. However, the extensive manual labeling required for large datasets of respiratory sounds presents a significant barrier, traditionally dependent on expert input. To address this, we introduce HEALSound, a mobile application designed to blend educational training with crowdsourced data labeling. By engaging users in interactive auscultation exercises and providing immediate feedback, HEALSound promotes skill development while generating high-quality labeled data through weighted consensus methods. Experimental results highlight the app's dual effectiveness: enhancing diagnostic learning for users and accelerating the development of machine-learning models through enriched datasets, thus addressing critical challenges in AI-based health diagnostics.",
keywords = "AI-based diagnostics, Digital stethoscope, adventitious sounds, crowdsourcing, data labeling, gamified learning, medical education",
author = "Yichen Long and Changyan Chen and Huajie Huang and Xuya Jiang and Qing Zhang and Yuhang Zhang and Jian Zhao and Rui Pan and Yongfu Li",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025 ; Conference date: 25-05-2025 Through 28-05-2025",
year = "2025",
doi = "10.1109/ISCAS56072.2025.11043827",
language = "英语",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCAS 2025 - IEEE International Symposium on Circuits and Systems, Proceedings",
address = "美国",
}