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

Objective.Robotic hand prosthesis users often identify the lack of physiological feedback as a major obstacle to seamless integration. Both the low controllability and high cognitive load required to operate these devices generally lead to their rejection. Consequently, experts highlight sensory feedback as a critical missing features of commercial prostheses. Providing feedback that promotes the integration of artificial limbs is often sought through a biomimetic paradigm, limited by the current technological landscape and the absence of neural embodiment in users. As a result, some researchers are now turning to bio-inspired approaches, choosing to repurpose existing neural structures and focusing on underlying neurocognitive mechanisms that promote the integration of artificial inputs.Approach.Taking a bio-inspired approach, this paper describes the first implementation of a somatotopic, non-invasive proprioceptive feedback strategy for hand prosthesis users, developed using a standard sensory restoration architecture, i.e. pre-processing, encoding and stimulation. The main hypothesis investigated is whether a novel use of transcutaneous electrical stimulation can be leveraged to deliver proprioceptive information of the hand to the user.Main results.The potential of the proposed strategy was highlighted via experimental validation in conveying specific finger apertures and grasp types related to single and multiple degrees of freedom. Six participants were able to identify apertures conveyed by median and ulnar nerve stimulation with an accuracy of 96.5% ± 2.3% and a response time of 0.91 s ± 0.08 s, as well as grasp types conveyed from concurrent median and ulnar nerve stimulation with an accuracy of 88.3% ± 1.2% and a response time of 0.44 s ± 0.27 s through 5 sets of 10 trials.Significance.These results demonstrate the relevance of a somatotopic proprioception feedback strategy for users of prosthetic hands, and the architecture presented in this case study allows for future optimization of the various sub-components.