# Flexible Peripheral Nerve Interfacing Electrode for Joint Position Control in Closed-Loop Neuromuscular Stimulation

## TL;DR

A new flexible nerve electrode system improves joint position control in neuroprosthetics by using advanced modeling and closed-loop stimulation.

## Contribution

A novel surface-based inverse recruitment model using barycentric coordinates for precise joint position control via nerve stimulation.

## Key findings

- The model achieved reduced settling time (<1.63 s), faster rising time (<0.39 s), and smaller steady-state error (<3 degrees) in rabbit ankle joint trials.
- Integration with PID control and flexible electrodes enhances stability and compatibility with neuroprosthetic systems.
- The system shows potential for advanced neuroprosthetic applications in managing neurological disorders.

## Abstract

Addressing peripheral nerve disorders with electronic medicine poses significant challenges, especially in replicating the dynamic mechanical properties of nerves and understanding their functionality. In the field of electronic medicine, it is crucial to design a system that thoroughly understands the functions of the nervous system and ensures a stable interface with nervous tissue, facilitating autonomous neural adaptation. Herein, we present a novel neural interface platform that modulates the peripheral nervous system using flexible nerve electrodes and advanced neuromodulation techniques. Specifically, we have developed a surface-based inverse recruitment model for effective joint position control via direct electrical nerve stimulation. Utilizing barycentric coordinates, this model constructs a three-dimensional framework that accurately interpolates inverse isometric recruitment values across various joint positions, thereby enhancing control stability during stimulation. Experimental results from rabbit ankle joint control trials demonstrate our model’s effectiveness. In combination with a proportional–integral–derivative (PID) controller, it shows superior performance by achieving reduced settling time (less than 1.63 s), faster rising time (less than 0.39 s), and smaller steady-state error (less than 3 degrees) compared to the legacy model. Moreover, the model’s compatibility with recent advances in flexible interfacing technologies and its integration into a closed-loop controlled functional neuromuscular stimulation (FNS) system highlight its potential for precise neuroprosthetic applications in joint position control. This approach marks a significant advancement in the management of neurological disorders with advanced neuroprosthetic solutions.