53
Research Trials
20
Peer-reviewed publications
16
Clinical Conditions
This narrative review by Hyfe's R&D team makes the case that continuous cough monitoring (CCM), powered by acoustic AI, transforms cough from a subjective symptom into a quantifiable digital biomarker.
This ERS 2025 abstract from Hyfe's R&D team validates the wear-detection algorithm built into the Hyfe CoughMonitor smartwatch against participant-reported wear status across 418 person-hours of data in ten participants.
Authored by Hyfe's R&D team, this review synthesizes work presented at the European Respiratory Society Congress 2025 and argues that objective cough monitoring has crossed a practical threshold, moving from experimental technique to deployable clinical endpoint.
This study asked whether the core components of BCST could be embedded in a digital therapeutic and paired with continuous, objective cough monitoring inside the CoughPro app.
01.10.2024
The ability to passively and continuously monitor cough would significantly improve cough management and research. Recent advances in acoustic AI have prompted the development of automated cough monitoring technology. Here we describe for the first time the process and results of validating an acoustic AI based cough monitor.
We collected 20-24 hours of continuous sounds from each of 23 adult subjects while they wore a Cough Monitor and went about their usual daily activities. The watch was charged <3 ft by the bedside at night, and manual cough counting is considered the gold standard. We noted the exact time of every cough in the continuous recording using a validated annotation methodology. These results were compared to the timestamps of cough from the Cough Monitor to determine the system’s performance for each subject, for the entire group, and for subsets using event-to-event and hourly rate correlation analyses.
In 546 hours of monitoring across 23 subjects, 4,454 coughs were detected by the trained annotators. Hyfe's CoughMonitor sensitivity was 90.4% (95% CI: 89.5-91.2%), with a false positive rate of 1.03/hour (95% CI: 0.94-1.11). Hourly cough rates showed a high correlation between manual counts and CoughMonitor data (Pearson r = 0.99, OLS slope = 0.94, intercept = 0.68).
The Cough Monitor's accuracy, ease of use, and scalability suggest it could significantly enhance cough management and research.
