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Related Concept Videos

Assessment of Respiration01:23

Assessment of Respiration

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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
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Respiratory System Abnormal Finding I: Inspection and Percussion01:30

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Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

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Assessment of Ventilation I: Respiratory Rate01:20

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Physical Assessment of the Respiratory Tract II: Inspection01:27

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Physical assessment of the respiratory tract through inspection is a crucial step in understanding the patient's respiratory health. It provides insights into the functioning of the respiratory system, the musculoskeletal structure, and even the patient's nutritional status. This comprehensive approach involves observing several vital aspects: chest configuration, breathing patterns, respiratory rates, skin color, and use of accessory muscles.
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Updated: Dec 19, 2025

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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An Adaptive Search Algorithm for Detecting Respiratory Artifacts Using a Wireless Passive Wearable Device.

P O'Neill1, W M Mongan1, R Ross1

  • 1College of Computing and Informatics: Drexel University, Philadelphia, PA USA.

... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a smart fabric sensor and algorithm to estimate respiratory activity using Radio Frequency Identification (RFID) signals. The method effectively identifies physiological signals amidst noise for real-time, noninvasive monitoring.

Keywords:
Adaptive Signal ProcessingBiomedical Signal ProcessingPrediction Methods

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Signal Processing

Background:

  • Wireless, wearable sensors are crucial for noninvasive physiological monitoring.
  • Radio Frequency Identification (RFID) signals can detect physiological states, but ambient noise poses a challenge.
  • Existing methods struggle to accurately identify physiological signals in noisy RFID data.

Purpose of the Study:

  • To develop a novel algorithm for estimating biomedical feedback, specifically respiratory activity, using a smart fabric strain gauge sensor.
  • To address the challenge of ambient noise artifacts in RFID signals for physiological monitoring.
  • To enable real-time, noninvasive prediction of physiological events for medical device actuation.

Main Methods:

  • Utilized a wireless, wearable, passive knitted smart fabric device as a strain gauge sensor.
  • Employed k-means clustering in a semi-unsupervised approach to identify repetitive physiological signals (respiratory activity) into 'active' and 'inactive' states.
  • Developed an algorithm to predict the next biomedical event based on classification results without complete noise removal.

Main Results:

  • The algorithm successfully estimated biomedical feedback, including respiratory activity.
  • The k-means clustering approach effectively identified physiological signals despite ambient noise.
  • The system predicted the onset of respiratory activity in a simulated environment within approximately one second.

Conclusions:

  • The proposed algorithm enables robust, real-time, noninvasive monitoring of physiological activity using smart fabric RFID sensors.
  • This technique facilitates the prediction of biomedical events, paving the way for advanced therapeutic medical devices.
  • The semi-unsupervised approach offers a practical solution for extracting meaningful physiological data from noisy wireless signals.