Improved Tactile Resonance Sensor for Robotic Assisted Surgery
This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour deli...
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description | This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. The results of the variance analysis are discussed showing that ability of the proposed tactile sensor to perform high quality measurements. |
doi_str_mv | 10.48550/arxiv.1805.00682 |
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The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. The results of the variance analysis are discussed showing that ability of the proposed tactile sensor to perform high quality measurements.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1805.00682</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Brain ; Differentiation (biology) ; Error detection ; Mechanical properties ; Nonlinear analysis ; Physics - Medical Physics ; Piezoelectricity ; Reliability analysis ; Robotic surgery ; Sensors ; Surgical instruments ; Tactile sensors (robotics) ; Tumors ; Variance analysis ; Viscoelasticity</subject><ispartof>arXiv.org, 2018-05</ispartof><rights>2018. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. The results of the variance analysis are discussed showing that ability of the proposed tactile sensor to perform high quality measurements.</description><subject>Brain</subject><subject>Differentiation (biology)</subject><subject>Error detection</subject><subject>Mechanical properties</subject><subject>Nonlinear analysis</subject><subject>Physics - Medical Physics</subject><subject>Piezoelectricity</subject><subject>Reliability analysis</subject><subject>Robotic surgery</subject><subject>Sensors</subject><subject>Surgical instruments</subject><subject>Tactile sensors (robotics)</subject><subject>Tumors</subject><subject>Variance analysis</subject><subject>Viscoelasticity</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj01rwkAURYdCoWL9AV010HXSyZuPzGwKIm0VhIJmH14mMyWiGTuTSP33jdrF424O975DyFNOM66EoK8YfttTlisqMkqlgjsyAcbyVHGABzKLcUcpBVmAEGxC3laHY_An2yQlmr7d22Rjo--wMzbZ2i76kLjxNr72fWuSeYxt7Ed6O4RvG86P5N7hPtrZf05J-fFeLpbp-utztZivUxTAU8GtM4XWteDYcMMKVoOUWtRgdY6OG81AqAJRC8UdKu2gZhKlvHBCNWxKnm-1V7nqGNoDhnN1kayukiPxciNGm5_Bxr7a-SF0408V0AJg3Mw5-wMRJVOo</recordid><startdate>20180502</startdate><enddate>20180502</enddate><creator>David Oliva Uribe</creator><creator>Schoukens, Johan</creator><creator>Stroop, Ralf</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>GOX</scope></search><sort><creationdate>20180502</creationdate><title>Improved Tactile Resonance Sensor for Robotic Assisted Surgery</title><author>David Oliva Uribe ; Schoukens, Johan ; Stroop, Ralf</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a524-54efc799b54ad4c373b26695b2e91af4c932587aa9584fa89f2b36a663b2658d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Brain</topic><topic>Differentiation (biology)</topic><topic>Error detection</topic><topic>Mechanical properties</topic><topic>Nonlinear analysis</topic><topic>Physics - Medical Physics</topic><topic>Piezoelectricity</topic><topic>Reliability analysis</topic><topic>Robotic surgery</topic><topic>Sensors</topic><topic>Surgical instruments</topic><topic>Tactile sensors (robotics)</topic><topic>Tumors</topic><topic>Variance analysis</topic><topic>Viscoelasticity</topic><toplevel>online_resources</toplevel><creatorcontrib>David Oliva Uribe</creatorcontrib><creatorcontrib>Schoukens, Johan</creatorcontrib><creatorcontrib>Stroop, Ralf</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>David Oliva Uribe</au><au>Schoukens, Johan</au><au>Stroop, Ralf</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved Tactile Resonance Sensor for Robotic Assisted Surgery</atitle><jtitle>arXiv.org</jtitle><date>2018-05-02</date><risdate>2018</risdate><eissn>2331-8422</eissn><abstract>This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. 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subjects | Brain Differentiation (biology) Error detection Mechanical properties Nonlinear analysis Physics - Medical Physics Piezoelectricity Reliability analysis Robotic surgery Sensors Surgical instruments Tactile sensors (robotics) Tumors Variance analysis Viscoelasticity |
title | Improved Tactile Resonance Sensor for Robotic Assisted Surgery |
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