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A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments.

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Τίτλος: A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments.
Συγγραφείς: Liu M; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China., Zhang Y; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China., Wang J; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China., Qin N; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China., Yang H; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China., Sun K; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China., Hao J; Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China., Shu L; Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China., Liu J; Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China., Chen Q; Shanghai Fire Research Institute of MEM, Shanghai, 200003, China., Zhang P; Suzhou Huiwen Nanotechnology Co., Ltd, Suzhou, 215004, China., Tao TH; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. tiger@mail.sim.ac.cn.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China. tiger@mail.sim.ac.cn.; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China. tiger@mail.sim.ac.cn.; 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. tiger@mail.sim.ac.cn.; School of Physical Science and Technology, ShanghaiTech University, Shanghai, 200031, China. tiger@mail.sim.ac.cn.; Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai, 200031, China. tiger@mail.sim.ac.cn.; Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, 200031, China. tiger@mail.sim.ac.cn.; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China. tiger@mail.sim.ac.cn.
Πηγή: Nature communications [Nat Commun] 2022 Jan 10; Vol. 13 (1), pp. 79. Date of Electronic Publication: 2022 Jan 10.
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Imprint Name(s): Original Publication: [London] : Nature Pub. Group
Ιατρικοί όροι (MeSH): Electronic Nose* , Machine Learning* , Pattern Recognition, Physiological*, Pattern Recognition, Automated/*methods, Moles/anatomy & histology ; Moles/physiology ; Odorants/analysis ; Animals ; Fires ; Humans ; Simulation Training
Περίληψη: Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short.
(© 2022. The Author(s).)
References: Nat Commun. 2022 Jan 10;13(1):79. (PMID: 35013205)
Nature. 2015 May 28;521(7553):436-44. (PMID: 26017442)
Nature. 2019 May;569(7758):698-702. (PMID: 31142856)
Comput Methods Programs Biomed. 2020 Sep;193:105479. (PMID: 32388066)
Sci Robot. 2018 Jun 27;3(19):. (PMID: 33141688)
Nature. 2015 Apr 30;520(7549):633-9. (PMID: 25896325)
Nanoscale. 2016 Jan 21;8(3):1430-6. (PMID: 26673658)
Nat Commun. 2019 Oct 30;10(1):4934. (PMID: 31666525)
Curr Opin Neurobiol. 2016 Oct;40:31-37. (PMID: 27344253)
Nat Methods. 2021 Feb;18(2):194-202. (PMID: 33479522)
ACS Appl Mater Interfaces. 2014 Sep 10;6(17):14975-80. (PMID: 25139871)
Sci Robot. 2020 Dec 16;5(49):. (PMID: 33328298)
ACS Appl Mater Interfaces. 2016 Apr 6;8(13):8652-61. (PMID: 26967662)
Cell. 2009 Oct 2;139(1):45-59. (PMID: 19804753)
IEEE Trans Image Process. 2010 Jun;19(6):1635-50. (PMID: 20172829)
Nanotechnology. 2015 Jul 10;26(27):275501. (PMID: 26066907)
Adv Mater. 2017 Feb;29(5):. (PMID: 27885730)
Nat Neurosci. 2001 Apr;4(4):353-4. (PMID: 11276222)
Sensors (Basel). 2011;11(3):2580-91. (PMID: 22163756)
Brain Struct Funct. 2015 Mar;220(2):955-77. (PMID: 24384580)
Nat Rev Neurosci. 2009 Mar;10(3):186-98. (PMID: 19190637)
Adv Mater. 2020 Apr;32(14):e1907043. (PMID: 31995260)
J Comp Neurol. 1995 Jan 23;351(4):549-67. (PMID: 7721983)
Sci Robot. 2019 Jan 30;4(26):. (PMID: 33137760)
ACS Appl Mater Interfaces. 2014 Aug 13;6(15):12429-35. (PMID: 25058566)
Entry Date(s): Date Created: 20220111 Date Completed: 20220126 Latest Revision: 20240405
Update Code: 20260130
PubMed Central ID: PMC8748716
DOI: 10.1038/s41467-021-27672-z
PMID: 35013205
Βάση Δεδομένων: MEDLINE
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  Data: A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments.
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  Data: <searchLink fieldCode="AU" term="%22Liu+M%22">Liu M</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Zhang+Y%22">Zhang Y</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Wang+J%22">Wang J</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Qin+N%22">Qin N</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.<br /><searchLink fieldCode="AU" term="%22Yang+H%22">Yang H</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Sun+K%22">Sun K</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Hao+J%22">Hao J</searchLink>; Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Shu+L%22">Shu L</searchLink>; Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Liu+J%22">Liu J</searchLink>; Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China.<br /><searchLink fieldCode="AU" term="%22Chen+Q%22">Chen Q</searchLink>; Shanghai Fire Research Institute of MEM, Shanghai, 200003, China.<br /><searchLink fieldCode="AU" term="%22Zhang+P%22">Zhang P</searchLink>; Suzhou Huiwen Nanotechnology Co., Ltd, Suzhou, 215004, China.<br /><searchLink fieldCode="AU" term="%22Tao+TH%22">Tao TH</searchLink>; State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. tiger@mail.sim.ac.cn.; School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China. tiger@mail.sim.ac.cn.; Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China. tiger@mail.sim.ac.cn.; 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China. tiger@mail.sim.ac.cn.; School of Physical Science and Technology, ShanghaiTech University, Shanghai, 200031, China. tiger@mail.sim.ac.cn.; Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai, 200031, China. tiger@mail.sim.ac.cn.; Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, 200031, China. tiger@mail.sim.ac.cn.; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China. tiger@mail.sim.ac.cn.
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  Data: <searchLink fieldCode="JN" term="%22101528555%22">Nature communications</searchLink> [Nat Commun] 2022 Jan 10; Vol. 13 (1), pp. 79. <i>Date of Electronic Publication: </i>2022 Jan 10.
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  Data: Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short.<br /> (© 2022. The Author(s).)
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  Label: References
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  Data: Nat Commun. 2022 Jan 10;13(1):79. (PMID: <searchLink fieldCode="PM" term="%2235013205%22">35013205)</searchLink><br />Nature. 2015 May 28;521(7553):436-44. (PMID: <searchLink fieldCode="PM" term="%2226017442%22">26017442)</searchLink><br />Nature. 2019 May;569(7758):698-702. (PMID: <searchLink fieldCode="PM" term="%2231142856%22">31142856)</searchLink><br />Comput Methods Programs Biomed. 2020 Sep;193:105479. (PMID: <searchLink fieldCode="PM" term="%2232388066%22">32388066)</searchLink><br />Sci Robot. 2018 Jun 27;3(19):. (PMID: <searchLink fieldCode="PM" term="%2233141688%22">33141688)</searchLink><br />Nature. 2015 Apr 30;520(7549):633-9. (PMID: <searchLink fieldCode="PM" term="%2225896325%22">25896325)</searchLink><br />Nanoscale. 2016 Jan 21;8(3):1430-6. (PMID: <searchLink fieldCode="PM" term="%2226673658%22">26673658)</searchLink><br />Nat Commun. 2019 Oct 30;10(1):4934. (PMID: <searchLink fieldCode="PM" term="%2231666525%22">31666525)</searchLink><br />Curr Opin Neurobiol. 2016 Oct;40:31-37. (PMID: <searchLink fieldCode="PM" term="%2227344253%22">27344253)</searchLink><br />Nat Methods. 2021 Feb;18(2):194-202. (PMID: <searchLink fieldCode="PM" term="%2233479522%22">33479522)</searchLink><br />ACS Appl Mater Interfaces. 2014 Sep 10;6(17):14975-80. (PMID: <searchLink fieldCode="PM" term="%2225139871%22">25139871)</searchLink><br />Sci Robot. 2020 Dec 16;5(49):. (PMID: <searchLink fieldCode="PM" term="%2233328298%22">33328298)</searchLink><br />ACS Appl Mater Interfaces. 2016 Apr 6;8(13):8652-61. (PMID: <searchLink fieldCode="PM" term="%2226967662%22">26967662)</searchLink><br />Cell. 2009 Oct 2;139(1):45-59. (PMID: <searchLink fieldCode="PM" term="%2219804753%22">19804753)</searchLink><br />IEEE Trans Image Process. 2010 Jun;19(6):1635-50. (PMID: <searchLink fieldCode="PM" term="%2220172829%22">20172829)</searchLink><br />Nanotechnology. 2015 Jul 10;26(27):275501. (PMID: <searchLink fieldCode="PM" term="%2226066907%22">26066907)</searchLink><br />Adv Mater. 2017 Feb;29(5):. (PMID: <searchLink fieldCode="PM" term="%2227885730%22">27885730)</searchLink><br />Nat Neurosci. 2001 Apr;4(4):353-4. (PMID: <searchLink fieldCode="PM" term="%2211276222%22">11276222)</searchLink><br />Sensors (Basel). 2011;11(3):2580-91. (PMID: <searchLink fieldCode="PM" term="%2222163756%22">22163756)</searchLink><br />Brain Struct Funct. 2015 Mar;220(2):955-77. (PMID: <searchLink fieldCode="PM" term="%2224384580%22">24384580)</searchLink><br />Nat Rev Neurosci. 2009 Mar;10(3):186-98. (PMID: <searchLink fieldCode="PM" term="%2219190637%22">19190637)</searchLink><br />Adv Mater. 2020 Apr;32(14):e1907043. (PMID: <searchLink fieldCode="PM" term="%2231995260%22">31995260)</searchLink><br />J Comp Neurol. 1995 Jan 23;351(4):549-67. (PMID: <searchLink fieldCode="PM" term="%227721983%22">7721983)</searchLink><br />Sci Robot. 2019 Jan 30;4(26):. (PMID: <searchLink fieldCode="PM" term="%2233137760%22">33137760)</searchLink><br />ACS Appl Mater Interfaces. 2014 Aug 13;6(15):12429-35. (PMID: <searchLink fieldCode="PM" term="%2225058566%22">25058566)</searchLink>
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