Robust Multimodal Cough Detection With Optimized Out-of-Distribution Detection for Wearables

Abstract

Longitudinal and continuous monitoring of cough is crucial for early and accurate diagnosis of respiratory diseases. Recent developments in wearables provides at-home remote symptom monitoring with respect to more accurate and less frequent assessment in the clinics, but face practical challenges such as speech privacy, poor audio quality and background noise in uncontrolled real-world settings. This study addresses these challenges by developing and optimizing a 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. The system is optimized through training with an enhanced dataset and a weighted multi-loss approach for in-distribution classification, while OOD detection is improved by reconstructing training data components. Experiments demonstrate robustness across window sizes from 1–5 seconds and effectiveness at low audio sampling rates, where privacy is preserved. The optimized system achieves 90.1% accuracy with a cough F1 score of 0.75 at 16 kHz and 87.3% accuracy with an F1 score of 0.70 at 750 Hz, even with half the inference data being OOD. Most misclassifications arise from nonverbal sounds (e.g., sneezes, groans). Overall, the proposed Audio-IMU multimodal model with OOD detection significantly improves cough detection performance and offers a practical solution for real-world wearable applications.