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PPE Use in Healthcare Settings I: Donning01:22

PPE Use in Healthcare Settings I: Donning

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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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Related Experiment Video

Updated: Sep 28, 2025

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
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Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

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Learning garment manipulation policies toward robot-assisted dressing.

Fan Zhang1, Yiannis Demiris1

  • 1Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.

Science Robotics
|April 6, 2022
PubMed
Summary
This summary is machine-generated.

Assistive robots can now help individuals with severe upper limb mobility loss dress themselves using a novel pipeline. This system successfully dresses a medical manikin in a hospital gown with over 90% accuracy.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Assistive robots offer potential solutions for daily living activities for people with disabilities.
  • Robot-assisted dressing is particularly challenging due to complex deformable garment manipulation.
  • Individuals with complete loss of upper limb movement functionality could greatly benefit from such technology.

Purpose of the Study:

  • To develop and validate a robot-assisted dressing pipeline for individuals with complete upper limb paralysis.
  • To address challenges in learning garment manipulation policies and transferring them from simulation to the real world.
  • To achieve robust and automated dressing using a dual-arm robot.

Main Methods:

  • An active pre-grasp manipulation approach combining prehensile and nonprehensile actions was developed to prepare garments for grasping.
  • A contrastive neural network was used to bridge the sim-to-real gap by approximating garment physics and accounting for simulator inaccuracies.
  • The pipeline involved grasping, unfolding, navigating, and sequentially lifting the user's arms to don a hospital gown.

Main Results:

  • The proposed method enabled a dual-arm robot to dress a medical training manikin with back-opening hospital gowns.
  • A success rate exceeding 90% was achieved in the experimental validation.
  • The active pre-grasp manipulation approach reduced uncertainties associated with grasping-only actions.

Conclusions:

  • The developed dressing pipeline demonstrates a significant advancement in assistive robotics for individuals with severe upper limb impairments.
  • Effective sim-to-real transfer of deformable object manipulation policies was achieved, enhancing practical applicability.
  • The system shows high success rates, paving the way for real-world applications in assisted dressing.