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.
10.08.2025
Cough frequency is a clinically relevant biomarker, but current “gold standard” human annotation is operationally impractical beyond 24 hours. Hyfe’s analysis shows that sampling error from short observation windows is a greater source of inaccuracy than device misclassification.
A simulation of 1,000 chronic cough patients demonstrated:
Key takeaway: In highly variable physiological signals like cough, duration of observation outweighs per-event precision. Automated, wearable systems—even if imperfect—deliver more accurate, scalable, and clinically relevant estimates than short-term perfect systems.
Implications:
Bottom line: We do not need perfect accuracy to measure cough optimally—only sustained accuracy, long enough to reflect reality.
