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1、SmartphonesbasedOnlineActivityRecognitionforIndoorLocalizationusingDeepConvolutionalNeuralNetworkJunYang,KaiCheng,JianfanChen,BaodingZhouandQingquanLiShenzhenKeyLaboratoryofSpatialSmartSensingandServices,ShenzhenUniversityKeyLaboratoryforGeo-EnvironmentMonitoringofCoa
2、stalZoneoftheNationalAdministrationofSurveying,MappingandGeoInformationAbstract—Intheindoorenvironment,theactivityofthepedes-twostepstogetherwithaneuralnetworkabletoautomaticallytriancanreflectsomesemanticinformation,forexample,ifalearnproperfeature.user’sactivityisreco
3、gnizedastakingelevator,thelocationofNowadays,HARhasbeenwidelyusedinourdailylivesintheuserisinferredtobeintheelevator.Theseactivitiescanbeformsofvariousfields.Oneofthemostwidelyusedfieldsusedasthelandmarksforindoorlocalization.Thedevelopmentofsensortechnologyenhancesthesm
4、artphone’ssensingandiselderlymonitoring[1]–[3].Withtherapidincreaseofthecomputationalcapabilities.Usingthesmartphones,theactivityagedpopulationinourcountry,thehealthcareinstitutionsisofthepedestriancanberecognized.Currentmethodsrelyonunderverylargepressureforthehighhea
5、lthcarecostoftheextractingcomplexhand-craftedfeatures,thusleadingtotheelders.HARwhichprovidethemedicalstaffwithmorecrucialincapabilityofrealtimepedestrianactivitiesidentification.Inthisinformationofparticularpatientsforaccidentscangreatlypaper,weproposearealtimepedestri
6、anactivitiesrecognitionmethodbasedondeepconvolutionalneuralnetwork.Anewdeepreducetheworkloadofmedicalinstitutions.Smarthomes[4]convolutionalneural(CNN)networkhasbeendesignedtolearnisanotherdomainsbenefitsfortheHARtechnology.Long-theproperfeaturesautomatically.Experiment
7、sshowthatthetermchronicsleepdisturbancesmaybringmedicalproblemsproposedmethodachievesapproximately95.21%accuracyinlikediabetes,obesity,andpsychologicalconditions,betterabout2secondsinidentifyingninetypesofactivities,includingtoolstoenableaccuratelong-termevaluationofsl
8、eeptimingstill,walk,upstairs,upelevator,upescalator,downelevator,downescalator,downstairsandturning.Besides,wetranspl