Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning-A Survey in...

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Machine Learning in Healthcare

Background:

• Surface electromyography (sEMG) signals are crucial for decoding human movement intentions in prosthetic and assistive devices.
• Current machine learning (ML) and deep learning (DL) control schemes face limitations in robustness and reliability due to complex upper-limb movements and sEMG signal instability.
• Developing multi-functional human-machine interfaces (HMIs) is essential for restoring lost upper-limb functions in disabled individuals.

Purpose of the Study:

• To systematically review recent advancements in ML/DL techniques for decoding sEMG signals for upper-limb functional reconstruction.
• To analyze strategies for enhancing the robustness, adaptation, and reliability of ML/DL-based control schemes.
• To identify future research challenges and opportunities in hardware, data resources, and decoding strategies.

Main Methods:

• Review of recent achievements in multi-modal sensing fusion to integrate diverse user information.
• Analysis of transfer learning (TL) methods designed to mitigate domain shift impacts on estimation models.
• Examination of post-processing approaches aimed at improving the reliability of decoded movement intentions.

Main Results:

• Multi-modal sensing fusion offers enhanced information for more accurate movement intention decoding.
• Transfer learning (TL) techniques effectively address domain shift issues, improving model generalizability.
• Post-processing methods and confidence estimation contribute to more reliable outcomes in sEMG-based control.

Conclusions:

• Significant progress has been made in improving the usability of ML/DL-based control schemes for upper-limb functional reconstruction.
• Future developments require advancements in hardware, accessible public datasets, and sophisticated decoding strategies.
• Integrating multi-modal sensing, advanced TL, and robust post-processing holds promise for next-generation HMIs.