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.

Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displacement, flatline behavior, saturation, abrupt autoscaling, outliers, and non-physiological noise. These low-quality segments can prevent reliable extraction of clinically relevant biomarkers, including heart rate, heart rate variability, pulse rate, oxygen saturation, and electrodermal activity, thereby limiting the robustness of downstream sleep stage classification and sleep apnea prediction. In this work, we present a multistage hybrid anomaly detection framework for signal quality assessment in a custom-designed multimodal wearable sleep monitoring platform. The proposed framework is shared across modalities: each sensor stream is segmented into windows, screened using signal processing algorithms for obvious sensor failures, and then analyzed using an autoencoder trained on normal windows to detect subtler morphology-level deviations from normal physiological patterns. On chest ECG recordings collected over 10 overnight sessions, the hybrid fusion detector achieves an accuracy of 0.9067, AUROC of 0.9068, F1 score of 0.8739, precision of 0.9470, and recall of 0.8202, outperforming rule-based detection and autoencoder-based detection alone. Additional experiments on PPG, EDA and ExG recordings show consistently high F1 scores of 0.9431, 0.9567 and 0.8728, respectively. These results demonstrate that hybrid anomaly detection can serve as an effective quality-control layer for multimodal wearable sleep monitoring and provide a foundation for future work on robust sleep stage and apnea prediction under real-world sensing conditions.

Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displacement, flatline behavior, saturation, abrupt autoscaling, outliers, and non-physiological noise. These low-quality segments can prevent reliable extraction of clinically relevant biomarkers, including heart rate, heart rate variability, pulse rate, oxygen saturation, and electrodermal activity, thereby limiting the robustness of downstream sleep stage classification and sleep apnea prediction. In this work, we present a multistage hybrid anomaly detection framework for signal quality assessment in a custom-designed multimodal wearable sleep monitoring platform. The proposed framework is shared across modalities: each sensor stream is segmented into windows, screened using signal-processing algorithms for obvious sensor failures, and then analyzed using an autoencoder trained on normal windows to detect subtler morphology-level deviations from normal physiological patterns. On chest ECG recordings collected over 10 overnight sessions, the hybrid fusion detector achieves an accuracy of 0.9204, AUROC of 0.9484, F1 score of 0.9108, precision of 0.9572, and recall of 0.8687, outperforming rule-based detection and autoencoder-based detection alone. Additional experiments on PPG, EDA, and ExG recordings show consistently high F1 scores of 0.9632, 0.9668, and 0.9779, respectively. These results demonstrate that hybrid anomaly detection can serve as an effective quality-control layer for multimodal wearable sleep monitoring and provide a foundation for future work on robust sleep stage and apnea prediction under real-world sensing conditions.