TY - GEN
T1 - Live Demonstration
T2 - 2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025
AU - Jiang, Xuya
AU - Chen, Changyan
AU - Long, Yichen
AU - Huang, Huajie
AU - Zhang, Qing
AU - Zhang, Yuhang
AU - Zhao, Jian
AU - Pan, Rui
AU - Li, Yongfu
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Healthcare Education and Labeling for Cardiopulmonary Sounds, HEALSound, is an interactive platform designed to enhance the identification and labeling of adventitious cardiopulmonary sounds through gamification. HEALSound enables users, particularly medical professionals, to engage in exercises that improve their knowledge of abnormal heart and lung sounds while simultaneously contributing to the labeling of raw audio data. By incorporating real-time feedback and progress tracking, the app promotes continuous learning and provides a valuable crowdsourced resource for building high-quality labeled datasets over time. This dual-purpose platform not only aids in medical education but also contributes to the advancement of machine learning models for sound classification in healthcare. The system demonstrates a new potential for significant impact in educational and clinical environments by seamlessly integrating learning with data collection, ensuring scalability and the continuous improvement of cardiopulmonary sound databases.
AB - Healthcare Education and Labeling for Cardiopulmonary Sounds, HEALSound, is an interactive platform designed to enhance the identification and labeling of adventitious cardiopulmonary sounds through gamification. HEALSound enables users, particularly medical professionals, to engage in exercises that improve their knowledge of abnormal heart and lung sounds while simultaneously contributing to the labeling of raw audio data. By incorporating real-time feedback and progress tracking, the app promotes continuous learning and provides a valuable crowdsourced resource for building high-quality labeled datasets over time. This dual-purpose platform not only aids in medical education but also contributes to the advancement of machine learning models for sound classification in healthcare. The system demonstrates a new potential for significant impact in educational and clinical environments by seamlessly integrating learning with data collection, ensuring scalability and the continuous improvement of cardiopulmonary sound databases.
UR - https://www.scopus.com/pages/publications/105010645989
U2 - 10.1109/ISCAS56072.2025.11043642
DO - 10.1109/ISCAS56072.2025.11043642
M3 - 会议稿件
AN - SCOPUS:105010645989
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - ISCAS 2025 - IEEE International Symposium on Circuits and Systems, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 May 2025 through 28 May 2025
ER -