Evaluating Populations of Tactile Sensors for Curvature Discrimination.
Identifieur interne : 001123 ( PubMed/Corpus ); précédent : 001122; suivant : 001124Evaluating Populations of Tactile Sensors for Curvature Discrimination.
Auteurs : Isabelle I. Rivest ; Gregory J. GerlingSource :
- Proceedings / Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems [ 1551-5435 ] ; 2010.
Abstract
The high density of receptors in fingertip skin is a limiting factor for replicating tactile feedback for neural prosthetics. At present, the large size of engineered sensors and the dense network of neural connections from finger to brain inhibit duplicating the approximately 100 receptors/cm(2). The objective of this work is to build a model of the skin and neural response with which populations of sensors can be positioned and evaluated when discriminating spheres. The effort combines a 3D finite element model of the fingertip, a bi-phasic transduction model, and a leaky-integrate-and-fire neuronal model. Populations of sensors are configured with three average densities (10,000/cm(2), 1,000/cm(2), and 100/cm(2)). For these populations, the firing rates for the dynamic (40-70 ms) and static (650 ms-900 ms) phases and first spike latencies are predicted. The model can differentiate indenters at a level similar to human performance at each sampling density, including of the human finger (100/cm(2)).
DOI: 10.1109/HAPTIC.2010.5444679
PubMed: 21814635
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Gerling</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2010">2010</date><idno type="doi">10.1109/HAPTIC.2010.5444679</idno><idno type="RBID">pubmed:21814635</idno><idno type="pmid">21814635</idno><idno type="wicri:Area/PubMed/Corpus">001123</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Evaluating Populations of Tactile Sensors for Curvature Discrimination.</title><author><name sortKey="Rivest, Isabelle I" sort="Rivest, Isabelle I" uniqKey="Rivest I" first="Isabelle I" last="Rivest">Isabelle I. Rivest</name><affiliation><nlm:affiliation>Systems and Information Engineering, University of Virginia, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Gerling, Gregory J" sort="Gerling, Gregory J" uniqKey="Gerling G" first="Gregory J" last="Gerling">Gregory J. Gerling</name></author></analytic><series><title level="j">Proceedings / Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems</title><idno type="ISSN">1551-5435</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The high density of receptors in fingertip skin is a limiting factor for replicating tactile feedback for neural prosthetics. At present, the large size of engineered sensors and the dense network of neural connections from finger to brain inhibit duplicating the approximately 100 receptors/cm(2). The objective of this work is to build a model of the skin and neural response with which populations of sensors can be positioned and evaluated when discriminating spheres. The effort combines a 3D finite element model of the fingertip, a bi-phasic transduction model, and a leaky-integrate-and-fire neuronal model. Populations of sensors are configured with three average densities (10,000/cm(2), 1,000/cm(2), and 100/cm(2)). For these populations, the firing rates for the dynamic (40-70 ms) and static (650 ms-900 ms) phases and first spike latencies are predicted. The model can differentiate indenters at a level similar to human performance at each sampling density, including of the human finger (100/cm(2)).</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">21814635</PMID><DateCreated><Year>2011</Year><Month>8</Month><Day>4</Day></DateCreated><Article PubModel="Print"><Journal><ISSN IssnType="Print">1551-5435</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2010</Year><Month>Mar</Month><Day>25</Day></PubDate></JournalIssue><Title>Proceedings / Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems</Title><ISOAbbreviation>Proc Symp Haptic Interface Virtual Env Teleoperator Syst</ISOAbbreviation></Journal><ArticleTitle>Evaluating Populations of Tactile Sensors for Curvature Discrimination.</ArticleTitle><Pagination><MedlinePgn>59-62</MedlinePgn></Pagination><Abstract><AbstractText>The high density of receptors in fingertip skin is a limiting factor for replicating tactile feedback for neural prosthetics. At present, the large size of engineered sensors and the dense network of neural connections from finger to brain inhibit duplicating the approximately 100 receptors/cm(2). The objective of this work is to build a model of the skin and neural response with which populations of sensors can be positioned and evaluated when discriminating spheres. The effort combines a 3D finite element model of the fingertip, a bi-phasic transduction model, and a leaky-integrate-and-fire neuronal model. Populations of sensors are configured with three average densities (10,000/cm(2), 1,000/cm(2), and 100/cm(2)). For these populations, the firing rates for the dynamic (40-70 ms) and static (650 ms-900 ms) phases and first spike latencies are predicted. 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