2025-10-13 ノースカロライナ州立大学(NC State)
<関連情報>
- https://news.ncsu.edu/2025/10/cough-detection-tech/
- https://ieeexplore.ieee.org/document/11189986
- https://medibio.tiisys.com/109643/
ウェアラブル端末向けに最適化された分布外検出機能を備えた堅牢なマルチモーダル咳検出 Robust Multimodal Cough Detection with Optimized Out-of-Distribution Detection for Wearables
Yuhan Chen; Feiya Xiang; Michelle L. Hernandez; Delesha Carpenter; Alper Bozkurt; Edgar Lobaton
IEEE Journal of Biomedical and Health Informatics Published:02 October 2025
DOI:https://doi.org/10.1109/JBHI.2025.3616945
Abstract:
Longitudinal and continuous monitoring of cough is crucial for early and accurate diagnosis of respiratory diseases. While recent developments in wearables offer a promise for daily assessment at-home remote symptom monitoring with respect to more accurate and less frequent assessment in the clinics, important practical challenges exist such as maintaining user speech privacy and potential poor audio quality and background noise in uncontrolled real-world settings. This study addresses these challenges by developing and optimizing a compact multimodal cough detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. The cough sensing modalities include audio and Inertial Measurement Unit (IMU) signals. We optimized this multimodal cough detection system by training with an enhanced dataset and employing a weighted multi-loss approach for the ID classifier. For OOD detection, we improved the system by reconstructing the training data components. Our preliminary results indicate the robustness of the system across window sizes from 1 to 5 seconds and performs efficiently at low audio frequencies, which can protect user privacy due to illegibility or incomprehensibility at lower sampling rates. Although we found that the multimodal model is sensitive to OOD data, the final optimized robust multimodal cough detection system outperforms the singlemodal model integrated with OOD detection. Specifically, the optimized system maintains 90.08% accuracy and a cough F1 score of 0.7548 at a 16 kHz audio frequency, and 87.3% accuracy and a cough F1 score of 0.7015 at 750 Hz, even with half of the data being OOD during inference. The misclassified components mainly originate from nonverbal sounds, including sneezes and groans. These issues could be further mitigated by acquiring more data on cough, speech, and other nonverbal vocalizations. In general, we observed that the Audio-IMU multimodal model incorporating OOD detection techniques significantly improved cough detection performance and could provide a tangible solution to real-world acoustic cough detection problems.
