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Page 466 Wang et al. Art Int Surg. 2025;5:465-75 https://dx.doi.org/10.20517/ais.2025.03

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.

Keywords: Artificial intelligence, prosthesis, robotic prosthesis, artificial neural networks, machine learning, EMG
signals, deep learning, pattern recognition

INTRODUCTION
The sudden loss of an upper limb is devastating, often resulting in functional and vocational impacts on
affected individuals. In the United States alone, there are 2.3 million individuals living with limb loss, with
approximately 185,000 new amputations occurring each year . Clinical research has shown that user
[1]
acceptance of prostheses depends mainly on the type of prosthesis, user training, and the control strategy
employed . There have been rapid advancements in the development of articulated and lifelike prosthetic
[2,3]
limbs over the past two decades, including myoelectric prosthetics, hybrid-assistive-limb systems,
[4,5]
exoskeletons, neural-controlled prosthetics, and robotic prosthetics . Despite all these advancements, a
seamless interface that allows users to truly mirror natural movement has yet to be developed.

Regenerative Peripheral Nerve Interface (RPNI) surgery is a procedure performed to amplify efferent motor
action potentials from peripheral nerves in the residual limbs of people with limb loss, thereby enhancing
prosthetic control. RPNI was developed in the Neuromuscular Laboratory at the University of Michigan.
This surgical procedure involves harvesting a 3 cm × 1.5 cm × 0.5 cm autologous free skeletal muscle graft
and wrapping it around the terminal end of a peripheral nerve or its individual fascicles . The muscle graft,
[6]
which can be harvested from the amputation site or a distant location, undergoes a process of degeneration,
regeneration, and reinnervation . The graft relies on imbibition in the initial stages postoperatively,
[7]
[8]
followed by inosculation and ultimately revascularization . During regeneration, the denervated muscle
graft is reinnervated by the nerve or fascicle, leading to the formation of functional neuromuscular
junctions. The peripheral nerve carries relatively low-amplitude efferent motor action potentials, which
transmit signals through the newly formed neuromuscular junctions to cause RPNI muscle contraction. The
peripheral nerve signals are then recorded from the RPNI, rather than directly from the peripheral nerve, to
create a more favorable signal-to-noise ratio (SNR).

RPNI signals are stable over the long term, which can be attributed to several biological mechanisms. First,
the reinnervated muscle graft contains functional neuromuscular junctions that remain stable over time,
enabling consistent generation of compound muscle action potentials (CMAPs). Second, robust
revascularization of the muscle graft ensures sustained tissue viability and prevents signal degradation over
time. Together, these features create a biologically stable, self-contained unit that continues to produce
high-quality, volitional signals for prosthetic control [9,10] . Electrodes implanted within the RPNI then record
these amplified CMAPs, enabling precise prosthetic control . The RPNI effectively acts as a bioamplifier
[11]
and signal transducer for peripheral nerve signals, improving SNRs and facilitating highly specific and
reliable prosthetic control.

While RPNI surgery provides an excellent method for generating control signals, the clinical challenge
remains complex. For instance, the median nerve controls multiple functions at the same time, including
index, middle, and ring finger flexion, as well as wrist flexion [12,13] . This results in a multitude of overlapping
signals that must be accurately interpreted from a single nerve to predict the user’s intended actions. The
challenge lies in interpreting these signals so that they can be used to control advanced multi-articulated