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An Adaptively Parameterized Algorithm Estimating Respiratory Rate from a Passive Wearable RFID Smart Garment.

Robert Ross1, William M Mongan1, Patrick O'Neill2

  • 1Drexel University, College of Engineering, Philadelphia, PA USA.

Proceedings : Annual International Computer Software and Applications Conference. COMPSAC
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

A new algorithm enhances the Bellyband, a wireless smart garment sensor, for accurate respiratory rate monitoring. This innovation offers a faster, more precise, and less invasive alternative to traditional wired sensors.

Keywords:
biomedical signal processingfiltering algorithmsparameter estimationwearable sensors

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Signal Processing

Background:

  • Wired respiratory rate sensors present limitations including patient tethering, potential body obscuration, and issues with accuracy or invasiveness.
  • The Bellyband, a smart garment sensor utilizing wireless, passive Radio Frequency Identification (RFID), offers a less invasive solution but faces signal processing challenges due to noisy, quantized data.

Purpose of the Study:

  • To develop and validate an advanced algorithm for estimating respiratory rate from the Bellyband sensor's bio-signals.
  • To improve the accuracy, speed, and real-time applicability of respiratory rate detection from the Bellyband compared to existing methods.

Main Methods:

  • An adaptively parameterized Savitzky-Golay (SG) filter was employed to smooth the noisy signals from the Bellyband.
  • The algorithm's adaptive parameterization allows for effective processing across a range of respiratory frequencies, including sharp changes.
  • Data from 25 trials, collected using an off-the-shelf respiratory monitor and metronome-synchronized breathing, were used for testing.

Main Results:

  • The new algorithm demonstrated a significant improvement, being three times faster and three times more accurate than the previous Bellyband algorithm.
  • Respiratory rate estimates achieved an average Root Mean Square Error (RMSE) of 4.1 breaths per minute (BPM) against ground truth.
  • Preliminary findings indicate potential for accuracy comparable to or exceeding established Electrocardiogram (ECG) or Impedance Plethysmography (IP) based methods for non-ventilated patients.

Conclusions:

  • The developed algorithm effectively processes noisy RFID signals from the Bellyband for accurate respiratory rate estimation.
  • This advancement offers a promising, real-time, wireless, and less invasive solution for respiratory monitoring.
  • The algorithm shows potential to rival current standards in respiratory rate detection for non-ventilated patients.