Somatotopic non-invasive proprioceptive feedback strategy for prosthetic hands: a preliminary study

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Technology

Background:

• Robotic hand prosthesis users face challenges with low controllability and high cognitive load due to the lack of physiological feedback.
• Sensory feedback is a critical missing feature in current commercial prostheses, hindering seamless integration and leading to device rejection.
• Bio-inspired approaches repurpose neural structures to promote the integration of artificial inputs, focusing on neurocognitive mechanisms.

Purpose of the Study:

• To implement and validate a somatotopic, non-invasive proprioceptive feedback strategy for hand prosthesis users.
• To investigate the efficacy of transcutaneous electrical nerve stimulation (TENS) in delivering proprioceptive information from a prosthetic hand.
• To address the critical need for enhanced sensory feedback in prosthetic devices.

Main Methods:

• Development of a sensory restoration architecture involving pre-processing, encoding, and stimulation.
• Implementation of a bio-inspired, somatotopic feedback strategy using TENS to deliver proprioceptive information.
• Experimental validation with six participants assessing the identification of finger apertures and grasp types.

Main Results:

• Participants achieved high accuracy (96.5% ± 2.3%) in identifying finger apertures via median and ulnar nerve stimulation.
• Accurate identification of grasp types (88.3% ± 1.2%) was achieved using concurrent median and ulnar nerve stimulation.
• The system demonstrated effective conveyance of proprioceptive information for both single and multiple degrees of freedom.

Conclusions:

• The study demonstrates the relevance of a somatotopic proprioception feedback strategy for prosthetic hand users.
• The developed architecture provides a foundation for future optimization of sensory feedback components in prosthetics.
• This bio-inspired approach shows promise for improving the integration and usability of robotic hand prostheses.