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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Continuous Semi-autonomous Prosthesis Control Using a Depth Sensor on the Hand.

Miguel Nobre Castro1, Strahinja Dosen1

  • 1Neurorehabilitation Systems, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.

Frontiers in Neurorobotics
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-autonomous control system for myoelectric prostheses, using a single depth sensor for intuitive grasp control. The system effectively assists users in grasping objects, even in cluttered environments, with minimal training.

Keywords:
computer visiongraspingmyoelectric hand prosthesisobject segmentationpoint cloud processingsemi-autonomous control

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Modern myoelectric prostheses offer advanced functionalities but lack intuitive user control.
  • Semi-autonomous control shows promise but often requires extensive sensor placement on the user.
  • Existing systems struggle with complex grasping tasks and cluttered environments.

Purpose of the Study:

  • To develop and evaluate a semi-autonomous control system for myoelectric prostheses using a single depth sensor.
  • To enable intuitive control for grasping diverse objects in various scenarios.
  • To assess the system's effectiveness and the impact of training on user interaction.

Main Methods:

  • A novel semi-autonomous control system was designed, incorporating a single depth sensor on the prosthesis.
  • The system automatically pre-shapes the hand for grasp type, size, and wrist rotation.
  • Online interaction between the user and prosthesis allowed continuous adjustment and aiming.

Main Results:

  • Experimental assessment with ten able-bodied participants demonstrated significant improvement in task completion time (TAT) across training blocks.
  • The system proved robust and effective in grasping objects individually and within cluttered scenes.
  • All participants successfully learned to aim and operate the prosthesis after brief training.

Conclusions:

  • The proposed single-depth-sensor system offers a robust and effective solution for semi-autonomous myoelectric prosthesis control.
  • The system facilitates intuitive grasping of objects in complex environments, reducing the need for extensive user-side sensors.
  • This represents a significant advancement towards self-contained, clinically applicable semi-autonomous prosthetic systems.