PPG-based algorithm achieves 98.93% accuracy in detecting device wear status with negligible battery impact, eliminating the critical problem of misclassifying patient stillness as non-compliance in continuous monitoring applications.
Simulation evidence supporting continuous, automated monitoring as a superior approach for clinical research.
A study demonstrating the use of synchronized wearables to determine when a cough monitor detects non-user coughs
Addressing privacy risks in clinical trials - edge computing and on-device cough analytics safeguard participant privacy, ensure regulatory compliance, and optimize clinical trial scalability.
27.09.2024
This paper explores the problem of detecting coughs from other people in shared environments when using wearable devices for health monitoring. We present a novel solution using machine learning models to classify coughs as "near" (from the user) or "far" (from others) based on acoustic properties. We collected a unique dataset of coughs recorded at different distances and trained models to capture spectral differences. Among the models tested, convolutional neural networks (CNNs) demonstrated exceptional performance, achieving a 0.94 ROC-AUC score in distinguishing between the user’s own coughs and those of others nearby.
The paper emphasizes the importance of accurate cough detection in remote health monitoring, particularly for patients with chronic respiratory diseases like COPD or during infectious disease outbreaks such as COVID-19. To enhance detection, the paper suggests integrating additional data streams—such as heart rate and motion sensors from wearable devices—to verify that a detected cough originated from the user. This multi-modal approach has the potential to improve the precision of cough monitoring, making the technology more reliable for real-world healthcare applications, from personalized treatment to population-level disease tracking.
