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Wang et al. Art Int Surg. 2025;5:465-75 Artificial
DOI: 10.20517/ais.2025.03
Intelligence Surgery

Review Open Access

Machine learning for Regenerative Peripheral Nerve
Interface-based prosthetic control: current

applications and clinical translation

1
1,2
2
1
Melanie J. Wang , Luis H. Cubillos , Theodore A. Kung , Stephen W.P. Kemp , Alison K. Snyder-
1
Warwick , Paul S. Cederna 1,2
1
Department of Surgery, Section of Plastic Surgery, University of Michigan, Ann Arbor, MI 48109, USA.
2
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Correspondence to: Dr. Paul S. Cederna, Department of Plastic Surgery, University of Michigan, Ann Arbor, MI 48109, USA. E-
mail: cederna@med.umich.edu
How to cite this article: Wang MJ, Cubillos LH, Kung TA, Kemp SWP, Snyder-Warwick AK, Cederna PS. Machine learning for
Regenerative Peripheral Nerve Interface-based prosthetic control: current applications and clinical translation. Art Int Surg.
2025;5:465-75. https://dx.doi.org/10.20517/ais.2025.03

Received: 13 Jan 2025 First Decision: 31 Jul 2025 Revised: 29 Aug 2025 Accepted: 3 Sep 2025 Published: 10 Oct 2025

Academic Editor: Zain Khalpey Copy Editor: Xing-Yue Zhang Production Editor: Xing-Yue Zhang

Abstract
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

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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