Machine learning for Regenerative Peripheral Nerve Interface-based prosthetic control: current applications and clinical translation

Machine learning algorithms and control systems have changed the design of modern-day prosthetic devices. This narrative review explores the evolution and application of machine learning in advanced prosthetic devices. Despite all the advancements created in prosthetic technology over the years, we still have not achieved the necessary level of functional rehabilitation or a seamless interface that allows users to truly mirror natural movement. Challenges persist in creating intuitive control strategies that can both interpret complex neural signals and translate them into fluid, multi-articulated movements. There is a need for better control strategies for these advanced prosthetic devices. Regenerative Peripheral Nerve Interface (RPNI) surgery has emerged in the field as a promising new way of enhancing prosthetic functionality. However, significant work is still needed to bridge the gap between current capabilities and the seamless, intuitive control required for naturalistic movement and true prosthetic embodiment. For continuous control, Kalman and Wiener filters have successfully translated EMG signals into smooth finger movements. In a study with rhesus macaques, a Kalman filter-based system achieved closed-loop continuous hand control using RPNI signals. For pose identification, Naïve Bayes (NB) classifiers and Hidden Markov Models combined with NB (HMM-NB) have shown high accuracy. One study reported > 96% accuracy in classifying finger movements using a NB classifier in rhesus macaques with RPNIs. In human participants, researchers decoded five different finger postures using only RPNI signals, both offline and in real time. Long-term stability of RPNI-based control has been demonstrated, with controllers maintaining high accuracy using calibration data collected up to 246 days prior. In a practical application, a human participant with RPNIs successfully completed a Coffee Making Task using four distinct grip patterns, showcasing the system’s functional utility.