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Related Concept Videos

Personal Protective Equipment01:20

Personal Protective Equipment

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Personal protective equipment (PPE) is unique clothing or equipment worn by an employee to minimize or prevent exposure to infectious agents. PPE creates a barrier between the employee and the infectious materials. PPE must be readily available in the patient care area. PPE includes gloves, gowns and aprons, masks and respirators, goggles, face shields, shoes, and headcovers:
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PPE Use in Healthcare Settings I: Donning01:22

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Donning PPE must be completed before contact with the patient. This process protects from infectious agents. The sequence and action included in each donning are critical, and the steps must be systematic to avoid exposure to pathogens. The institutional policy also needs to be followed while donning PPE. The pre-donning preparations are gathering equipment, inspecting the PPE equipment for tears, holes, or damage, removing jewelry, removing any garments below the elbows, and tying the hair...
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PPE Use in Healthcare Settings II: Doffing01:10

PPE Use in Healthcare Settings II: Doffing

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The sequence of removing or doffing PPE starts with the gloves, as they are the most contaminated. Next is removal of the face shield or goggles, as they would interfere with removing other PPE. Then remove the gown, followed by the mask or respirator. Perform hand hygiene between steps if hands become contaminated and immediately after removing all PPE. Generally, the outside front and sleeves of the isolation gown, the goggles or the mask, the respirator, and the face shield are contaminated.
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Oxygen Delivering System II: Venturi Mask and Transtracheal Oxygen01:16

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Oxygen therapy is a pivotal aspect of medical care, particularly for patients with respiratory ailments. Two prominent oxygen-delivering systems include the Venturi mask and the transtracheal oxygen catheter.
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Tracheostomy care is an essential nursing skill that involves cleaning and maintaining a tracheostomy tube to prevent infection and other complications. Here's a step-by-step guide explaining each procedure with its rationale. Note that disposable gloves are to be worn at all times and changed as often as needed to maintain a sterile work environment, and to protect both patient and healthcare worker.
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Cardiopulmonary Resuscitation II: ACLS Airway Management01:22

Cardiopulmonary Resuscitation II: ACLS Airway Management

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Airway management is a key skill in emergency and critical care settings, as maintaining a clear airway is essential for adequate oxygenation and ventilation.Head Tilt-Chin Lift TechniqueThe head tilt-chin lift maneuver is an essential technique primarily used in patients without suspected cervical spine injuries. To perform this maneuver, one hand is placed on the patient’s forehead, and gentle pressure is applied backward to tilt the head. The fingertips of the other hand are positioned...
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Detecting and Salvaging Head Impacts with Decoupling Artifacts from Instrumented Mouthguards.

Ryan Gellner1, Mark T Begonia2, Matthew Wood2

  • 1Virginia Tech (Biomedical Engineering and Mechanics), Blacksburg, VA, USA. gryan3@vt.edu.

Annals of Biomedical Engineering
|February 8, 2025
PubMed
Summary

Instrumented mouthguards (iMGs) can inaccurately record head impacts due to decoupling. This study developed a machine learning model to detect and correct these artifacts, improving head impact data accuracy.

Keywords:
ArtifactDecouplingDetectionInstrumented mouthguardMachine learningSalvage

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Area of Science:

  • Biomechanics
  • Sports Medicine
  • Data Science

Background:

  • Repetitive head impacts and concussions are linked to long-term health issues.
  • Instrumented mouthguards (iMGs) are used to measure head impacts in sports.
  • Data artifacts from iMG decoupling compromise measurement accuracy.

Purpose of the Study:

  • To develop a machine learning algorithm for predicting iMG decoupling.
  • To compare the algorithm's performance against existing methods.
  • To present a method for salvaging decoupled iMG signals.

Main Methods:

  • Recreated iMG decoupling artifacts in a laboratory setting.
  • Identified time, frequency, and time-frequency features of decoupled impacts.
  • Developed and tested a machine learning classifier on laboratory and field data.

Main Results:

  • The machine learning algorithm accurately predicted iMG decoupling.
  • The developed salvaging method reduced peak resultant error in decoupled signals.
  • The algorithm outperformed several proprietary and published decoupling detection methods.

Conclusions:

  • Machine learning models can effectively predict and mitigate iMG decoupling artifacts.
  • A salvaging technique can improve the accuracy of compromised head impact data.
  • Combining methods may enable comprehensive identification and correction of iMG data artifacts.