AI Article Synopsis

  • The development of a wearable device called "VitalCore" aims to continuously monitor vital parameters like respiration and pulse through a T-shirt, addressing the challenges of long-term health monitoring.
  • VitalCore uses advanced polymer-based sensors to accurately capture breathing patterns, achieving high sensitivity and precision during sleep assessments and light exercise.
  • Bench tests show that the device maintains data quality across various sleeping positions and demonstrates strong performance in identifying respiratory peaks, making it a promising tool for diagnosing respiratory and cardiovascular diseases.

Article Abstract

The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. To address these requirements, we have developed "VitalCore", a wearable continuous vital parameter monitoring device in the form of a T-shirt targeting the uninterrupted monitoring of respiration, pulse, and actigraphy. VitalCore uses polymer-based stretchable resistive bands as the primary sensor to capture breathing and pulse patterns from chest expansion. The carbon black-impregnated polymer is implemented in a U-shaped configuration and attached to the T-shirt with "interfacing" material along with the accompanying electronics. In this paper, VitalCore is bench tested and compared to gold standard respiration and pulse measurements to verify its functionality and further to assess the quality of data captured during sleep and during light exercise (walking). We show that these polymer-based sensors could identify respiratory peaks with a sensitivity of 99.44%, precision of 96.23%, and false-negative rate of 0.557% during sleep. We also show that this T-shirt configuration allows the wearer to sleep in all sleeping positions with a negligible difference of data quality. The device was also able to capture breathing during gait with 88.9%-100% accuracy in respiratory peak detection.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146453PMC
http://dx.doi.org/10.3390/s20061583DOI Listing

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