A Low Power Integrated Sensor Node Design for Real-Time Psychophysiological Monitoring

Technology #ua17-015

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Researchers
Janet Roveda
Associate Professor, Electrical & Computer Engineering
Richard Lane
Professor, Psychiatry
Kemeng Chen
Graduate student, Electrical & Computer Engineering
Managed By
Lewis Humphreys
Licensing Manager (520) 626-2574

Title: A Low Power Integrated Sensor Node Design for Real-Time Psychophysiological Monitoring

 

Invention: This technology is a design for enhanced noise reduction algorithms that allow for the acquisition of electrocardiogram (ECG), respiration, and movement data continuously for at least 24 hours. Additionally, the firmware on the microcontroller is integrated with the Vagus Relax smartphone app.

 

Background: Existing physiological monitoring devices regularly lose data and rarely collect consistent streams of data over longer time periods due to inefficient noise reduction algorithms. In order for medical technology to meet the needs of the growing percentage of the population that is elderly, it is likely that physiological monitoring devices will be integrated with smartphones in the future. This invention’s algorithms allow for the collection of uninterrupted ECG data for over 24 hours.

Applications:

  • Consumers in need of monitoring and/or decreasing their stress levels

Advantages:

  • Collects a continuous stream of ECG data
  • Integrated with the Vagus Relax smartphone app
  • Does not require a third party to provide sensors

 

Licensing Manager:

Lewis Humphreys

LewisH@tla.arizona.edu

(520) 626-1213