Live Demonstration: Crowdsourcing Cardiopulmonary Sound Labeling via Gamified Interactive Learning (HEALSound)

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.

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Publication Details

Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11043642
Citations
1
Primary Topic
Phonocardiography and Auscultation Techniques
Type
article
Field-Weighted Citation Impact
1.52

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article

Live Demonstration: Crowdsourcing Cardiopulmonary Sound Labeling via Gamified Interactive Learning (HEALSound)

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1 citations
Phonocardiography and Auscultation Techniques
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article

Live Demonstration: Crowdsourcing Cardiopulmonary Sound Labeling via Gamified Interactive Learning (HEALSound)

Rui Pan, Huajie Huang, Jian Zhao, Changyan Chen, Xuya Jiang, Yuhang Zhang, Qing Zhang, Yongfu Li, Long Yi
article en
1 citations

Abstract

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.

Shanghai Jiao Tong University (CN)
Shanghai Jiao Tong University, Research and Development
Openalex Percentile: Top 17%
Phonocardiography and Auscultation Techniques
1.52
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