Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Human-Machine Interfaces

Background:

• Advanced human-machine interfaces are crucial for amputees controlling prostheses.
• Current myoelectric signal control (MSC) methods face challenges in functionality, robustness, and adaptability.
• Dexterous operation, stability against confounding factors, and long-term user adaptation are key research areas.

Purpose of the Study:

• To review the state-of-the-art and limitations of current myoelectric signal control (MSC) methods for upper-limb prostheses.
• To analyze approaches for prosthetic hand control, focusing on functionality, robustness, and adaptability.
• To identify research focuses and clinical usability of MSC methods.

Main Methods:

• Review of different prosthetic hand control approaches (DOF configuration, discrete/simultaneous control).
• Analysis of factors affecting control performance stability (limb position, electrode shift, force variance, inadvertent activity).
• Examination of strategies for automatic classifier adaptation for individual users and long-term use.

Main Results:

• Identified various methods for prosthetic hand control and their performance metrics (accuracy, response, intuitiveness).
• Detailed confounding factors that impact the stability and reliability of myoelectric control.
• Highlighted strategies for adaptive MSC systems to enhance long-term usability.

Conclusions:

• Current MSC methods offer a foundation but require further development for optimal clinical application.
• Addressing functionality, robustness, and adaptability is key to improving prosthetic control for amputees.
• This review guides future research towards more intuitive, stable, and user-friendly myoelectric prostheses.