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.
Exploring integration of AI-driven cough monitoring into wearable technology, highlighting its potential as an early indicator of respiratory illness, immune stress, and chronic disease, while addressing technical feasibility, clinical validation, and future applications in personalized health & welness
Case studies on digital behavioral interventions for chronic cough management inside CoughPro, demonstrating promising reductions in cough frequency through AI-powered monitoring and science-based suppression techniques.
White paper on the connection between heart rate and cough severity. Integrating cardiovascular and respiratory metrics offers new possibilities for patient monitoring, clinical trials, and health technology innovation.
7-day continuous cough monitoring outperforms 24-hour methods. Hyfe white paper presents data-driven evidence showing how prolonged monitoring provides more reliable insights for clinical trials and research studies, offering a new standard in understanding cough variability
Exploring advanced machine learning models as solutions to the "Other Peoples Cough Problem" - distinguishing user coughs from others in shared environments.
Explores the cost-effectiveness of at-home monitoring for Chronic Obstructive Pulmonary Disease (COPD) exacerbations using a cough monitoring system. It highlights the significant economic burden COPD imposes on healthcare systems, especially through acute exacerbations that often lead to costly hospitalizations.
Explores the complexities of defining and measuring cough bouts using continuous monitoring technology. It highlights the inadequacies of traditional definitions and emphasizes the need for patient-centered metrics to better capture the severity and impact of chronic coughing on individuals' quality of life.
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.
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.
