Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Engineering

Background:

• Human hand control relies on complex neuro-muscular mechanisms.
• Current prosthetic hands offer limited functionality due to noisy surface EMG signals.
• Intramuscular EMG signals provide higher quality data for improved prosthetic control.

Purpose of the Study:

• To evaluate 14 common algorithms for direct and proportional prosthetic hand control.
• To establish a baseline performance metric for prosthetic hand control algorithms.
• To compare estimated hand forces using iEMG algorithms against measured forces.

Main Methods:

• Applied 14 algorithms to intramuscular EMG (iEMG) signals.
• Estimated forces generated by the prosthetic hand.
• Compared estimated forces to ground truth measured forces using a recently published iEMG database.

Main Results:

• Identified and evaluated the performance of 14 common iEMG signal processing algorithms.
• Established a baseline performance metric for direct and proportional prosthetic hand control.
• Demonstrated the potential of iEMG signals for more robust prosthetic control.

Conclusions:

• Intramuscular EMG signal processing algorithms show promise for enhancing prosthetic hand control.
• The evaluated algorithms provide a benchmark for future prosthetic control strategies.
• Further research can leverage these findings for developing more sophisticated prosthetic systems.