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Smart beds can detect COVID-19 symptoms by analyzing sleep and cardiorespiratory data. This technology offers a non-invasive method for early illness detection and monitoring, supporting public health surveillance.

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Area of Science:

  • Digital Health
  • Biomedical Engineering
  • Infectious Disease Surveillance

Background:

  • Viral infections like COVID-19 impact sleep and cardiorespiratory function.
  • Consumer smart bed technology enables unobtrusive, real-world monitoring of sleep and physiological signals.
  • Smart beds offer an alternative to wearables and self-reports for objective data capture.

Purpose of the Study:

  • To use smart bed ballistocardiography (BCG) signals and predictive modeling for detecting and monitoring COVID-19 symptoms.
  • To leverage objective, longitudinal biometric data for individual-level illness tracking.

Main Methods:

  • Retrospective analysis of 1725 US adults using smart bed data (pulse rate, respiratory rate, sleep metrics).
  • A two-stage machine learning pipeline: symptom detection and illness-symptom progression models.
  • Within-subject comparisons of symptomatic versus baseline periods using Gaussian Mixture Hidden Markov Models.

Main Results:

  • The model detected COVID-19 symptoms in 104 out of 122 positive cases.
  • Significant deviations in sleep and cardiorespiratory metrics were observed during symptomatic periods (AUC=0.80).
  • The model demonstrated high discriminatory performance in identifying illness windows.

Conclusions:

  • Smart beds provide a valuable resource for passive, objective, longitudinal data collection.
  • Findings support the feasibility of using smart bed data and ML for real-time COVID-19 detection.
  • Future work includes model refinement and application for population-level infectious disease surveillance.