Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.
Background: The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfo...
| Publicado en: | BioMedical Engineering OnLine Vol. 18; no. 1 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
4/8/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135796203&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135796203 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 4/8/2019 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 135796203 135796203 NLM30961620 10.1186/s12938-019-0659-9 NLM30961620 135796203 ppct: 1 formats: tig: atl: Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses. aug: au: Petrini, Francesco M. Mazzoni, Alberto Rigosa, Jacopo Giambattistelli, Federica Granata, Giuseppe Barra, Beatrice Pampaloni, Alessandra Guglielmelli, Eugenio Zollo, Loredana Capogrosso, Marco Micera, Silvestro Raspopovic, Stanisa affil: Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, TAN E 2, Tannenstrasse 1, 8092, Zurich, Switzerland sug: subj: Median Nerve Physiology Hand Innervation Prosthesis Design Algorithms Hand Motor Neurons Hand Physiology Fingers Physiology Ultrasonography Movement Muscles Physiology Female Male Fingers Scales Female Male ab: Background: The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements.Findings: To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand-related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed.Conclusions: Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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