Evaluating Populations of Tactile Sensors for Curvature Discrimination
Identifieur interne : 001E93 ( Pmc/Checkpoint ); précédent : 001E92; suivant : 001E94Evaluating 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/cm2. 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/cm2, 1,000/cm2, and 100/cm2). 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/cm2).
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DOI: 10.1109/HAPTIC.2010.5444679
PubMed: 21814635
PubMed Central: 3147307
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<record><TEI><teiHeader><fileDesc><titleStmt><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></author><author><name sortKey="Gerling, Gregory J" sort="Gerling, Gregory J" uniqKey="Gerling G" first="Gregory J." last="Gerling">Gregory J. Gerling</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21814635</idno><idno type="pmc">3147307</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3147307</idno><idno type="RBID">PMC:3147307</idno><idno type="doi">10.1109/HAPTIC.2010.5444679</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">001166</idno><idno type="wicri:Area/Pmc/Curation">001166</idno><idno type="wicri:Area/Pmc/Checkpoint">001E93</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">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></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">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">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<sup>2</sup>. 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<sup>2</sup>, 1,000/cm<sup>2</sup>, and 100/cm<sup>2</sup>). 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<sup>2</sup>).</p></div></front></TEI><pmc article-type="research-article" xml:lang="en"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101536400</journal-id><journal-id journal-id-type="pubmed-jr-id">38271</journal-id><journal-id journal-id-type="nlm-ta">Proc Symp Haptic Interface Virtual Env Teleoperator Syst</journal-id><journal-title-group><journal-title>Proceedings / Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems</journal-title></journal-title-group><issn pub-type="ppub">1551-5435</issn></journal-meta><article-meta><article-id pub-id-type="pmid">21814635</article-id><article-id pub-id-type="pmc">3147307</article-id><article-id pub-id-type="doi">10.1109/HAPTIC.2010.5444679</article-id><article-id pub-id-type="manuscript">NIHMS309799</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Evaluating Populations of Tactile Sensors for Curvature Discrimination</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Rivest</surname><given-names>Isabelle I.</given-names></name><xref rid="FN1" ref-type="author-notes">1</xref></contrib><contrib contrib-type="author"><name><surname>Gerling</surname><given-names>Gregory J.</given-names></name><xref rid="FN2" ref-type="author-notes">2</xref></contrib><aff id="A1">Systems and Information Engineering, University of Virginia, USA</aff></contrib-group><author-notes><corresp id="FN1"><label>1</label><email>iir2u@virginia.edu</email></corresp><corresp id="FN2"><label>2</label><email>gregory-gerling@virginia.edu</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>16</day><month>7</month><year>2011</year></pub-date><pub-date pub-type="ppub"><day>25</day><month>3</month><year>2010</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>8</month><year>2011</year></pub-date><fpage>59</fpage><lpage>62</lpage><permissions><copyright-statement>©2010 IEEE</copyright-statement><copyright-year>2010</copyright-year></permissions><abstract><p id="P1">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<sup>2</sup>. 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<sup>2</sup>, 1,000/cm<sup>2</sup>, and 100/cm<sup>2</sup>). 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<sup>2</sup>).</p></abstract><kwd-group><kwd>Tactile</kwd><kwd>touch</kwd><kwd>mechanoreceptor</kwd><kwd>solid mechanics</kwd><kwd>biomechanics</kwd><kwd>finite element analysis</kwd><kwd>leaky-integrate-and-fire</kwd><kwd>neural model</kwd><kwd>neural prosthetics</kwd></kwd-group><funding-group><award-group><funding-source country="United States">National Institute of Neurological Disorders and Stroke : NINDS</funding-source><award-id>R01 NS073119-01 || NS</award-id></award-group></funding-group></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Gerling, Gregory J" sort="Gerling, Gregory J" uniqKey="Gerling G" first="Gregory J." last="Gerling">Gregory J. Gerling</name><name sortKey="Rivest, Isabelle I" sort="Rivest, Isabelle I" uniqKey="Rivest I" first="Isabelle I." last="Rivest">Isabelle I. Rivest</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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