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Stable, Long-term Multi-grip Control Using Regenerative Peripheral Nerve Interfaces In Humans With Upper-limb Amputations
Philip P. Vu, PhD, Alex K. Vaskov, PhD, Theodore A. Kung, PhD, Cynthia A. Chestek, PhD, Paul S. Cederna, MD, Stephen W.P Kemp, PhD.
University of Michigan, Ann Arbor, MI, USA.

PURPOSE:State-of-the-art control systems can provide upper-extremity prosthetic users greater control, but rely on recording methods limited by low signal-to-noise ratios (SNRs). Consequently, these control systems require frequent recalibration, leading to user frustration and prosthesis abandonment. We developed the Regenerative Peripheral Nerve Interface (RPNI) as a more reliable recording interface for prosthetic control. An RPNI consists of a free muscle graft reinnervated by a transected peripheral nerve and produces SNRs that can be 12 x greater than other methods. In this study, we hypothesize that RPNI signal quality can remain stable over time and allow control systems to maintain prediction performance without recalibration.
METHODS:Two participants with transradial amputations (P1 and P2) underwent surgery to place RPNIs on each of their median and ulnar nerves in 2015 and 2017, respectively. In 2018, intramuscular bipolar electrodes were placed into their RPNIs. Participants were then instructed to volitionally move their phantom limb to mirror 2 finger movements (thumb flexion, small finger flexion). These tasks were repeated in 11 sessions over 276 days in P1 and 14 sessions over 422 days in P2 to track SNRs over time. Additionally, we trained a multi-grip (fist, pinch, point) classifier and tested prediction accuracy in several different arm postures (arm at side, arm raised, arm in front, and arm across body) without recalibration up to 478 days in P2.
RESULTS:In both participants, SNRs remained consistently high, ranging from 15-250 across sessions, and did not decrease over time. However, signals did vary substantially from session to session. Specifically, P1ís median RPNI SNRs ranged between 65-250 across days for thumb flexion, whereas the ulnar RPNI SNRs ranged between 15-55 for small finger flexion. P2ís SNRs ranged between 20-40 for the median RPNI and 15-80 for the ulnar RPNIs. The multi-grip classifier predicted greater than 94% accuracy across all arm postures with no significant decrease in performance across sessions (p = 0.26). Of the 1,179 trials performed, 85% of trials had prediction speeds of less than 250 ms, which is well below the 300 ms threshold of perceived delay between muscle activation and prosthetic hand movement.
CONCLUSION:RPNIs provide high signal quality and stability (SNRs > 15), allowing them to be used as prosthetic control systems to maintain prediction performance without recalibration for up to 478 days. As a result, RPNIs have the potential to increase prosthetic use satisfaction and decrease prosthetic abandonment.


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