Parallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition, Inspired by the Human Somatosensory System

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dc.contributor.authorLee, Gun-Heeko
dc.contributor.authorPark, Jin-Kwanko
dc.contributor.authorByun, Junyoungko
dc.contributor.authorYang, Jun Changko
dc.contributor.authorKwon, Se Youngko
dc.contributor.authorKim, Chobiko
dc.contributor.authorJang, Choromko
dc.contributor.authorSim, Joo Yongko
dc.contributor.authorYook, Jong-Gwanko
dc.contributor.authorPark, Steveko
dc.date.accessioned2020-05-14T08:20:16Z-
dc.date.available2020-05-14T08:20:16Z-
dc.date.created2019-12-30-
dc.date.created2019-12-30-
dc.date.created2019-12-30-
dc.date.created2019-12-30-
dc.date.issued2020-02-
dc.identifier.citationADVANCED MATERIALS, v.32, no.8-
dc.identifier.issn0935-9648-
dc.identifier.urihttp://hdl.handle.net/10203/274197-
dc.description.abstractInspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S-11) spectrum, and the applied pressure changes the magnitude of the S-11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S-11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability.-
dc.languageEnglish-
dc.publisherWILEY-V C H VERLAG GMBH-
dc.titleParallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition, Inspired by the Human Somatosensory System-
dc.typeArticle-
dc.identifier.wosid000502609400001-
dc.identifier.scopusid2-s2.0-85076780231-
dc.type.rimsART-
dc.citation.volume32-
dc.citation.issue8-
dc.citation.publicationnameADVANCED MATERIALS-
dc.identifier.doi10.1002/adma.201906269-
dc.contributor.localauthorPark, Steve-
dc.contributor.nonIdAuthorPark, Jin-Kwan-
dc.contributor.nonIdAuthorKim, Chobi-
dc.contributor.nonIdAuthorJang, Chorom-
dc.contributor.nonIdAuthorSim, Joo Yong-
dc.contributor.nonIdAuthorYook, Jong-Gwan-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorelectronic skin-
dc.subject.keywordAuthorLC passive resonators-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorparallel signal processing-
dc.subject.keywordAuthorpressure sensors-
dc.subject.keywordPlusPLASTICITY-
dc.subject.keywordPlusMECHANISMS-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordPlusSKIN-
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