Neuromimetic Event-Based Detection for Closed-Loop Tactile Feedback Control of Upper Limb Prostheses

Area of Science:

• Neuroscience
• Biomedical Engineering
• Robotics

Background:

• Upper limb amputees often lack tactile sensation, hindering environmental interaction.
• Restoring tactile feedback is crucial for improving prosthetic hand functionality.

Purpose of the Study:

• To develop and implement a closed-loop tactile feedback system for prosthetic hands using biomimetic sensors and neuromimetic algorithms.
• To assess the impact of this tactile feedback on grasping performance in able-bodied and amputee subjects.

Main Methods:

• Developed fingertip tactile sensors mimicking human neural spiking responses for detecting contact, release, and slip.
• Designed and implemented two neuromimetic algorithms: Compliant Grasping and Slip Prevention.
• Conducted grasping experiments comparing prosthetic hand performance with and without tactile feedback.

Main Results:

• Tactile feedback significantly reduced object breakage or slippage by over 50%.
• Grasping task completion time decreased by at least 10% in most trials.
• Both able-bodied and amputee subjects demonstrated improved performance with the neuromimetic feedback system.

Conclusions:

• Neuromimetic tactile sensing offers a novel approach to enhance upper limb prosthesis function.
• Biologically inspired tactile feedback improves the dexterity and reliability of prosthetic hands.
• This technology has the potential to significantly improve the quality of life for amputees.