Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes

Vu, Philip; Vaskov, Alex K.; Lee, Christina; Jillala, Ritvik; Wallace, Dylan; Davis, Alicia J.; Kung, Theodore A.; Kemp, Stephen W.P.; Gates, Deanna H.; Chestek, Cynthia A.; Cederna, Paul S.

doi:10.1088/1741-2552/accb0c

Cited by 20 publications

(6 citation statements)

References 96 publications

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“…In extraneural electrodes, amplitudes are small compared to intraneural amplitudes, they have a lower SNR, and increased artifacts due to surrounding EMG activation and electrode movement [132]. Extraneural and regenerative electrodes have been demonstrated to have long-term recording stability in animal studies [22,133] and human studies [47,134,135].…”

Section: Resolution and Specificity In Stimulation And Recordingmentioning

confidence: 99%

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Taghlabi,

Cruz-Garza,

Hassan

et al. 2024

J. Neural Eng.

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Peripheral nerve interfaces (PNI) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types of electrodes currently used for PNI systems and their functionalities and limitations. We discuss important design characteristics of PNI systems, including biocompatibility, resolution and specificity, efficacy, and longevity, to highlight their importance in the current and future development of PNIs. The clinical outcomes of PNI systems are also discussed. Finally, we review relevant PNI clinical trials that were conducted, up to the present date, to restore the sensory and motor function of upper or lower limbs in amputees, spinal cord injury (SCI) patients, or intact individuals and describe their significant findings. This review highlights the current progress in the field of PNIs and serves as a foundation for future development and application of PNI systems.

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Section: Resolution and Specificity In Stimulation And Recordingmentioning

confidence: 99%

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Taghlabi,

Cruz-Garza,

Hassan

et al. 2024

J. Neural Eng.

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“…It is worth noting that the RPNI technique has been assessed in various clinical studies involving humans, showcasing promising outcomes in alleviating neuropathic pain and in the application of myoelectric prostheses. Nevertheless, future researchers should prioritize addressing the dearth of clinical trials that substantiate these findings [12,50,54,57,58,64,65].…”

Section: Type Of Modelmentioning

confidence: 99%

Regenerative Peripheral Nerve Interfaces (RPNIs) in Animal Models and Their Applications: A Systematic Review

González-Prieto,

Cristóbal,

Arenillas

et al. 2024

IJMS

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Regenerative Peripheral Nerve Interfaces (RPNIs) encompass neurotized muscle grafts employed for the purpose of amplifying peripheral nerve electrical signaling. The aim of this investigation was to undertake an analysis of the extant literature concerning animal models utilized in the context of RPNIs. A systematic review of the literature of RPNI techniques in animal models was performed in line with the PRISMA statement using the MEDLINE/PubMed and Embase databases from January 1970 to September 2023. Within the compilation of one hundred and four articles employing the RPNI technique, a subset of thirty-five were conducted using animal models across six distinct institutions. The majority (91%) of these studies were performed on murine models, while the remaining (9%) were conducted employing macaque models. The most frequently employed anatomical components in the construction of the RPNIs were the common peroneal nerve and the extensor digitorum longus (EDL) muscle. Through various histological techniques, robust neoangiogenesis and axonal regeneration were evidenced. Functionally, the RPNIs demonstrated the capability to discern, record, and amplify action potentials, a competence that exhibited commendable long-term stability. Different RPNI animal models have been replicated across different studies. Histological, neurophysiological, and functional analyses are summarized to be used in future studies.

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“…Many myoelectric control systems use machine learning to map the electromyographic (EMG) signals [1][2][3][4][5] to control commands for human-machine interfaces, e.g. prosthesis [6][7][8][9][10][11][12] and virtual keyboards [13,14]. Most modern myoelectric control machine learning models require a large amount of data from a user to learn a bespoke and user-specific map [7,15,16].…”

Section: Introductionmentioning

confidence: 99%

One-shot random forest model calibration for hand gesture decoding

Jiang,

Ma,

Nazarpour

2024

J. Neural Eng.

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Objective: Most existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user. Approach: We trained a random forest model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model. Results: Real-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific random forest and the linear discriminant analysis models. Furthermore, the random forest model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach. Significance: The proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.

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Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes

Cited by 20 publications

References 96 publications

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Regenerative Peripheral Nerve Interfaces (RPNIs) in Animal Models and Their Applications: A Systematic Review

One-shot random forest model calibration for hand gesture decoding

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