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Related Experiment Video

Updated: Aug 8, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review.

Vaheh Nazari1, Yong-Ping Zheng1,2

  • 1Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

Sonomyography (SMG), an ultrasound-based human-machine interface, shows promise for controlling upper limb prostheses. Machine learning algorithms achieve high accuracy in classifying hand gestures using non-invasive sensors.

Keywords:
controlling systemhuman–machine interfacemachine learningnon-invasive sensorprosthesissonomyography

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Upper limb prostheses require intuitive control interfaces.
  • Sonomyography (SMG) offers a non-invasive approach using ultrasound.
  • Existing human-machine interfaces (HMIs) have limitations.

Purpose of the Study:

  • To critically review and compare recent studies on SMG for upper limb prosthesis control.
  • To evaluate ultrasound modes, feature extraction, and machine learning algorithms for HMIs.

Main Methods:

  • Systematic literature search on Google Scholar and PubMed.
  • Keywords: Human Machine Interface, Sonomyography, Ultrasound, Upper Limb Prosthesis, Artificial Intelligence, Non-Invasive Sensors.
  • Analysis of 59 selected articles, with 16 used for detailed comparison.

Main Results:

  • Ultrasound sensing is a viable HMI for controlling multi-degree-of-freedom bionic hands.
  • Machine learning algorithms achieve ~95% accuracy in classifying hand gestures.
  • Various ultrasound modes and feature extraction methods impact AI performance.

Conclusions:

  • Sonomyography presents a promising non-invasive technology for advanced prosthetic control.
  • Further research can optimize SMG systems for enhanced prosthetic functionality.
  • AI integration with SMG can lead to more natural and responsive prosthetic limbs.