Toward Impedance Control in Human-Machine Interfaces for Upper-Limb Prostheses.

Area of Science:

• Biomedical Engineering
• Robotics
• Neuroscience

Background:

• Upper-limb impedance adaptation is vital for human interaction with the environment during grasping and manipulation.
• Current prosthetic control methods often lack adaptability to user intent and task demands.

Purpose of the Study:

• To introduce a novel framework for Adaptive Impedance Control of Upper-limb Prosthesis (AIC-UP) utilizing surface electromyography (sEMG) signals.
• To enable intuitive adaptation of prosthesis impedance (stiffness, damping) based on estimated human motor intent.

Main Methods:

• AIC-UP employs muscle-tendon models driven by agonist-antagonist sEMG signals to estimate joint kinematics, stiffness, and damping.
• A variable impedance controller is implemented on a simulated robot, avoiding joint torque or stiffness measurements for calibration.
• The framework was evaluated with able-bodied subjects and a transradial amputee in simulated wrist flexion-extension tasks.

Main Results:

• AIC-UP demonstrated superior robustness to muscle coactivations compared to a neural network-based kinematic regression approach.
• User feedback corroborated the enhanced performance and control capabilities of AIC-UP.
• The system allowed users to effectively adapt joint stiffness and damping to specific task requirements.

Conclusions:

• AIC-UP provides a robust and adaptable control framework for upper-limb prostheses.
• The sEMG-driven approach enhances user control and interaction with prosthetic devices.
• This technology holds promise for improving functional outcomes for amputees.