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.
31.10.2025
Hyfe has developed and validated a lightweight, photoplethysmography (PPG)-based algorithm that accurately detects whether its CoughWatch smartwatch is being worn. This algorythm does not rely on accelerometer data. Traditional accelerometer-based methods misclassify stillness (e.g., resting subjects) as non-wear and require long detection windows.
The new algorithm activates the PPG sensor for just 20 seconds every 5 minutes, analyzes reflection data using a simple decision-tree model, and classifies wear status in real time. Trained on ca. 100 person-hours and validated across +2,700 hours of human-logged data, the model achieved 98.93% accuracy, with only 0.55% false negatives and 2.06% false positives. Battery impact is minimal, averaging 24.2% drain per 24 hours, consistent with baseline device performance.
This innovation enables high-frequency, low-burden, and precise detection of device adherence, strengthening data integrity in continuous physiological and behavioral monitoring, critical for clinical trials and longitudinal studies.
