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Page 470 Wang et al. Art Int Surg. 2025;5:465-75 https://dx.doi.org/10.20517/ais.2025.03

mirrored the classifier output without providing the NHP visual feedback during the trial) and closed-loop
(the virtual hand was controlled by the online classifier output, and the NHPs received feedback on the
screen regarding the predicted movement based on their RPNI signals) settings, achieving similar
performance to physical control (the virtual hand is controlled by the NHP’s actual movements). This
innovation leads to more intuitive and precise prosthetic control, improves signal stability over time, and
enables detailed extraction of motor intentions, enhancing both functionality and user experience.

These classifiers can quickly and accurately decode a range of hand movements, including individual finger
[32]
motions, wrist actions, and various grasp patterns. Vaskov et al. found that implanted electrodes recorded
high-quality EMG signals from RPNIs and residual innervated forearm muscles in two persons with
transradial amputations. The implant procedure targeted the same individual finger movements in both
participants. Using these signals, the participants were able to control a virtual hand to distinguish
individual finger, intrinsic, and grasp postures. They used a posture switching task to test real-time control.
In this task, participants controlled a virtual hand and attempted to match the posture of a cue hand. The
speed and accuracy of the pattern recognition system in distinguishing finger movements exceeded earlier
work [33-35] that quantified real-time performance in virtual environments. In a controlled environment, the
HMM-NB also distinguished a smaller set of functional postures in novel static arm positions. The
participants used the high-speed pattern recognition system to control advanced robotic prostheses,
eliminating the need for time-consuming grip triggers or selection schemes. The study also found that the
HMM-NB consistently improved simulated performance over NB and outperformed each alternate
classifier. This was because the HMM-NB could model transitions between latent states, which allowed it to
rapidly issue accurate predictions. The HMM-NB model’s ability to capture temporal dynamics offered a
distinct advantage in real-time applications, despite its higher computational load. The HMM-NB most
noticeably improved performance for smaller processing windows, which can increase responsiveness.
Specifically, for Patient 1 (P1), the simulated accuracy of HMM-NB was consistently higher than NB across
various processing window lengths, indicating an improvement in accuracy. For instance, at a 50-ms
processing window, HMM-NB achieved approximately 95% accuracy, while NB demonstrated 90%
accuracy. This represents an approximate 5 percentage point increase in accuracy for HMM-NB over NB.
Similarly, for Patient 2 (P2), HMM-NB was 94% accurate, which was consistently higher than the 85%
simulated accuracy of NB. HMM-NB consistently improved simulated performance over NB (P < 0.01,
paired t-test, n = 42 window lengths across 6 datasets). This improvement was particularly noticeable for
smaller processing windows, which can enhance responsiveness.

RPNI-based pose identification allows patients to perform complex tasks essential for daily living. Lee
[36]
et al. used an HMM classifier in a human participant with a unilateral transradial amputation, who had
RPNIs surgically implanted, to distinguish four functional grips (rest, fist, pinch, and point) with high
accuracy. RPNI controllers maintained high accuracy using calibration data collected up to 246 days prior,
showcasing the long-term stability of this approach. In a practical demonstration of prosthetic functionality,
the subject engaged in a comprehensive Coffee Making Task. This task involved manipulating various
objects associated with brewing coffee, including a water-filled cup (simulated with beads), a coffee pod,
sugar, and a compact Keurig™ coffee maker. The exercise required the utilization of four distinct grip
patterns to handle these items effectively. To assess performance, the subject executed the entire sequence
without interruption, allowing for measurement of total completion time. Additionally, the task was broken
down into five discrete segments, each focusing on transitioning to a specific grip pattern (e.g., forming a
fist to grasp the cup), to evaluate the accuracy of individual grip transitions. This practice demonstrated the
system’s utility in real-world scenarios.