A comparison between force and position control strategies in myoelectric prostheses.

Area of Science:

• Biomedical Engineering
• Rehabilitation Robotics
• Neuroprosthetics

Background:

• Myoelectric control is crucial for prosthetic limb function.
• Accurate estimation of both force and position is essential for intuitive prosthetic control.
• Current methods face challenges in simultaneously estimating multiple degrees of freedom (DOFs).

Purpose of the Study:

• To investigate simultaneous and proportional myoelectric force and position estimation for multiple DOFs in unilateral transradial amputees.
• To compare the performance of force and position control paradigms using EMG data.
• To train artificial neural networks (ANNs) for estimating limb dynamics from contralateral EMG signals.

Main Methods:

• Two experiments were conducted: isometric force contractions and dynamic position contractions.
• Electromyography (EMG) signals from the contralateral limb were recorded during mirrored bilateral contractions.
• ANNs were trained to predict force and position based on EMG patterns across three DOFs (wrist flexion/extension, radial/ulnar deviation, forearm supination/pronation).

Main Results:

• Force estimation using ANNs achieved high accuracy (R(2)=0.84±0.02).
• Position estimation performance was significantly lower (R(2)=0.57±0.05).
• The study highlights differential accuracy in estimating static force versus dynamic position from EMG.

Conclusions:

• Myoelectric force estimation from EMG signals shows promising accuracy for prosthetic applications.
• Position estimation remains a significant challenge, requiring further research for improved prosthetic control.
• This research provides insights into the capabilities and limitations of EMG-based control for multi-DOF prosthetic limbs.