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Related Concept Videos

Hand hygiene01:23

Hand hygiene

Asepsis is the practice of preventing or breaking the chain of infection. The nurse employs aseptic techniques to prevent the spread of microorganisms and reduce the risk of diseases. Hand hygiene is the cornerstone of aseptic techniques and is classified into medical and surgical asepsis. Medical asepsis includes hand hygiene and the use of gloves. Surgical asepsis, or the sterile technique, refers to practices that render and keep objects and areas free of microorganisms.
Hand washing...
Handwashing II: Pre-procedure and Initial Procedure Steps01:19

Handwashing II: Pre-procedure and Initial Procedure Steps

The pre-procedure steps of handwashing include removing jewelry and rolling up sleeves. However, many organizations allow staff to wear wedding rings.
The hand washing procedure itself includes the following steps. First, cover cuts, if any, on hands with a waterproof dressing. Cuts and abrasions can become contaminated with bacteria hindering the ability to clean the area thoroughly. In addition, repeated hand washing can worsen an injury.  The nails must be short and clean, without nail paint...
Handwashing III: During the Procedure and Post-Procedure Steps01:15

Handwashing III: During the Procedure and Post-Procedure Steps

To wash hands properly, follow these steps:

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Related Experiment Video

Updated: May 23, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Mixed Reality and Desktop Hand Hygiene Training With Deep Learning-Based Step Recognition and Real-Time Decision

Syed Muhammad Umair Arif, Tomiris Rakhimzhanova, Abylaikhan Myrzakhanov

    IEEE Transactions on Visualization and Computer Graphics
    |May 21, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a real-time hand hygiene (HH) training system using AI for quality assessment. Desktop displays offer better efficiency and stability than Mixed Reality (MR) for HH training.

    Related Experiment Videos

    Last Updated: May 23, 2026

    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
    06:28

    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

    Published on: August 26, 2018

    Area of Science:

    • Healthcare technology
    • Artificial intelligence in medicine
    • Infection control

    Background:

    • Conventional hand hygiene (HH) monitoring lacks detail on procedural quality and feedback.
    • Need for advanced systems to improve HH compliance and prevent healthcare-associated infections.

    Purpose of the Study:

    • To develop and evaluate a real-time HH training and assessment framework using deep learning and AI.
    • To compare the effectiveness of Desktop versus Mixed Reality (MR) interfaces and Concurrent-Feedback Coaching (CFC) versus Uncued Retention Assessment (URA) modes.

    Main Methods:

    • Developed a framework combining deep learning (YOLOv12-n+MV) for WHO HH step recognition with a decision support engine.
    • Deployed the system on Desktop LED and MR (HoloLens 2) platforms, supporting CFC and URA modes.
    • Conducted a mixed-methods study with 20 participants evaluating performance, usability, and workload.

    Main Results:

    • The compact YOLOv12-n+MV model achieved a favorable accuracy-efficiency trade-off for real-time HH recognition.
    • Desktop interfaces demonstrated significantly faster, more stable, and less error-prone HH performance compared to MR.
    • CFC mode reduced completion time, while URA mode reduced errors, indicating a speed-accuracy trade-off. Desktop also showed higher usability and lower workload.

    Conclusions:

    • Desktop displays are more suitable for HH training prioritizing efficiency, stability, and lower workload.
    • CFC and URA modes can be selectively applied in Desktop or MR workflows to enhance guided learning or independent recall.
    • The developed framework offers a novel approach to real-time, quality-focused HH training and assessment in healthcare settings.