Disambiguating Cough Provenance in Cohabiting Settings

This paper addresses a critical limitation in wearable-based cough monitoring: distinguishing coughs emitted by the user versus those from nearby individuals (related: Other People's Coughs). In a controlled experiment with cohabiting couples (n=4), synchronized smartwatches running Hyfe's cough detection software captured cough events alongside their root mean square (RMS) acoustic energy—a proxy for source proximity.

Results showed that the device worn by the cougher detected 100% of events with zero false positives, while the non-cougher’s device detected 95% of events, also without false positives. Crucially, the RMS amplitude was consistently and significantly higher on the source device (median RMS: 0.0925 vs. 0.0235; p < 0.001), enabling 100% correct attribution of coughs using a simple energy threshold.

This method offers a scalable, non-intrusive alternative to contact microphones or manual annotation, making accurate source attribution viable in decentralized trials and real-world settings. By leveraging synchronized acoustic data alone, the approach enhances the scientific rigor of remote respiratory monitoring - especially in shared environments where attribution has historically been uncertain.

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