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Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring.

Stephen Hansen1, Daniel Schwartz1, Jesse Stover1

  • 1Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA USA.

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering
|May 20, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a smart garment sensor using Radio Frequency Identification (RFID) to monitor infant breathing. This novel algorithm significantly improves signal clarity for more accurate respiratory rate detection.

Keywords:
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Area of Science:

  • Medical Internet of Things (IoT)
  • Wearable Biosensors
  • Signal Processing

Background:

  • The medical Internet of Things (IoT) requires unobtrusive, passively powered sensors for continuous health monitoring.
  • Monitoring infant respiratory activity is crucial for early detection of health issues.
  • Existing wearable sensors may face challenges with signal quality and artifact interference.

Purpose of the Study:

  • To develop and validate a novel algorithm for monitoring infant respiratory activity using wireless, wearable, and passive knitted smart garment sensors.
  • To improve the accuracy of respiratory rate detection by enhancing the signal-to-noise ratio (SNR) of Radio Frequency Identification (RFID) measurements.
  • To classify and separate respiratory activity from artifacts using advanced signal processing techniques.

Main Methods:

  • Utilized multi-tag RFID measurements from knitted smart garment sensors to capture infant respiratory data.
  • Employed fusion learning across multiple features from various RFID tags.
  • Developed a Regime Hidden Markov Model (HMM) incorporating higher-order Minkowski and Mahalanobis distance features for signal classification and artifact separation.

Main Results:

  • The developed algorithm significantly improved the average Signal to Noise Ratio (SNR) from 17.12 dB to 34.74 dB for respiratory rate detection.
  • Demonstrated the effectiveness of higher-order features in enhancing signal strength detection within RFID systems.
  • Successfully classified and separated respiratory activity, reducing noise and artifacts in the monitored data.

Conclusions:

  • The proposed algorithm enhances the utility of multi-tag RFID measurements for unobtrusive infant respiratory monitoring.
  • Higher-order features derived from Minkowski and Mahalanobis distances are effective in improving signal detection in RFID systems.
  • The algorithm shows potential for broader applications in machine learning for respiratory data classification and other biomedical signal processing tasks.