Vital Signs Monitoring

An electronic sensor pad embedded around a patient’s bed allows passive motion capture while patient sleeps.   This enables computing overnight heart and respiratory rate vital sign monitoring and recent trend data.

The sensor captures core vital signs and an array of motion data, designed to create an AI-enabled machine learning algorithm to predict a COPD exacerbation, days in advance.

We analyze

  • Heart rate (HR) trends

  • Respiratory rate (RR) trends

  • WaveForms of both HR and RR

  • Sleep disruption patterns

  • Other Physiologically Predictive Signals

Designed for Chronic Care

  • Fully passive: no patient dependencies

  • Once Installed, No User adjustments

  • Objective Measure of Physiologic State

  • Designed to Work across range of beds, BMIs, and sleep positions

  • Designed to address dual sleepers and common environmental interferences in the home

Designed to Scale

  • Designed to Supports Wi-Fi + cellular connections

  • Seamless EHR integration, HIPAA compliant cloud

Informed by Clinical Study

Detect-Ex Study (UK, Dr. John Hurst):

  • Targeted Enrollment 75 COPD patients with at least one exacerbation in past 12 months

  • Targeted One Year LDT Use

  • Daily Symptom and Exacerbation Rating Scores

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