外文翻译利用手腕麦克风和三轴加速计来进行手势定位.docx
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外文翻译利用手腕麦克风和三轴加速计来进行手势定位.docx
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外文翻译利用手腕麦克风和三轴加速计来进行手势定位
GestureSpottingUsingWristWornMicrophone
and3-AxisAccelerometer
(原文2)Abstract.Weperformcontinuousactivityrecognitionusingonlytwowrist-wornsensors-a3-axisaccelerometerandamicrophone.Webuildontheintuitivenotionthattwoverydierentsensorsareunlikelytoagreeinclassicationofafalseactivity.Bycomparingimperfect,slidingwindowclassicationsfromeachofthesesensors,weareablediscernactivitiesofinterestfromnulloruninterestingactivities.Whereonesensoraloneisunabletoperformsuchpartitioning,usingcomparisonweareabletoreportgoodoverallsystemperformanceofupto70%accuracy.Inpresentingtheseresults,weattempttogiveamore-indepthvisualizationoftheerrorsthancanbegatheredfromconfusionmatricesalone.
1Introduction
Handactionsplayacrucialroleinmosthumanactivities.Asaconsequencesdetectingandrecognisingsuchactivitiesisoneofthemostimportantaspectsofcontextrecognition.Atthesameitisoneofthemostdicult.Thisisparticularlytrueforcontinuousrecognitionwhereasetofrelevanthandmotions(gestures)needtobespottedinadatastream.Thedicultiesofsuchrecognitionstemfromtwothings.First,duetoalargenumberofdegreesoffreedom,handmotionstendtobeverydiverse.Thesameactivitymightbeperformedinmanydierentwaysevenbyasingleperson.Second,intermsofmotion,handsarethemostactivebodyparts.Wemoveourhandscontinuously,mostlyinanunstructuredway,evenwhennotdoinganythingparticularwiththem.Infactinmostsituationssuchunstructuredmotionsbyfaroutnumbergesturesthatarerelevantforcontextrecognition.Thismeansthatacontinuousgesturespottingapplicationshastodealwithanzeroclassthatisdiculttomodelwhiletakingupmostofthesignal.
1.1PaperContributions
Ourgrouphasinvestedaconsiderableamountofworkintohandgesturespotting.Todatethisworkhasfocusedonusingseveralsensorsdistributedovertheuserfibodytomaximisesystemperformance.Thisincludedmotionsensors(3axisaccelerometer,3axisgyroscopesand3axismagneticsensors)ontheupperandlowerarm[3],microphone/accelerometercombinationontheupperandlowerarm[5]aswellas,morerecently,acombinationofseveralmotionsensorsandultrasoniclocationdevices.Thispaperinvestigatestheperformanceofagesturespottingsystembasedonasingle,wristmounteddevice.
Theideabehindtheworkisthatwristmountedaccessoriesarebroadlyacceptedandwornbymostpeopleondailybasis.Incontrast,systemsthatrequiretheusertoputonseveralsensorsatlocationssuchastheupperarmwouldhavemuchmoreproblemswithuseracceptance.
Thedownsideofthisapproachisthereducedamountofinformationavailablefortherecognition.Thisforexamplemeansthatthemethodofanalysingsoundintensitydierencesbetweenmicrophonesondierentpartsofthebodythatwasthecornerstoneofourprevioussignalpartitioningworkisnotfeasible.Thisproblemiscompoundedbythefactthatfortheapproachtomakesensethatwristmounteddevicecanneithercontaintoomanysensorsnorcanitrequirecomputingand/orcommunicationpowerthatwouldimplylarge,bulkybatteries.
Themaincontributionofthepaperistoshowthat,foracertainsubsetofactivities,reasonablegesturespottingresultscanbeachievedwithacombinationofamicrophoneand3axisaccelerometermountedonthewrist.Ourmethodreliesonsimplejumpingwindowsoundprocessingalgorithmsthatwehaveshown[10]torequireonlyminimalcomputational
andcommunicationperformance.FortheaccelerationweuseinferenceonHiddenMarkovModels(HMM),againonjumpingwindowsacrossthedata.
Toourknowledgethisisthersttimethatsuchasimplesystemandastraightforwardjumpingwindowmethodhasbeensuccessfullyusedforhandgesturespottingincontinuousdatastreamwithadominant,unstructuredzeroclass.Previouslysuchsetupsandalgorithmshaveonlybeenshowntobesuccessfulleitherforsegmentedrecognitionorforscenarioswherethezeroclasswaseithereasytomodelornotrelevant(e.g.recognitionofstanding,sitting,walking,running[6,9,12]).Wheretheseapproachesuseaccelerationsensors,intheworkof[?
?
]soundwasexploitedforperformingsituationanalysisinthewearablecomputingdomain.Also[?
]usedsoundinformationtoimprovetheperformanceofhearingaids.Complimentaryinformationfromsoundandaccelerationhasbeenusedbeforetodetectdefectsinmaterialsurfaces,e.g.in[13],butnoworkthattheauthorsareawareusestheseforrecognitionofcomplexactivities.
Inthepaperwesummarisethesoundandaccelerationalgorithmsandthenfocusontheperformanceofdierentfusionmethods.Itisshownthatappropriatefusionisthekeytoachievinggoodperformancedespitesimplesensorsandalgorithms.Weverifyourapproachondatafromawoodworkshopassemblyexperimentthathavewehaveintroducedandusedinpreviouswork[5].Wepresenttheresultsusingbothtraditionalconfusionmatrices,plusanovelvisualisationmethodthatprovidesamorein-depthunderstandingoftheerrortypes.
2RecognitionMethod
Weapplyslidingwindowsoflenghtwsecondsacrossallthedatainincrementsofw.AteachstepweapplyanwjmpLDAbasedclassicationonthesounddata,andanHMMclassicationonthesound.Thefisoftresultsofeachclassication-LDAdistancesforsoundandHMMclasslikelihoodsforacceleration-areconvertedintoclassrankings,andthesearefusedtogetherusingoneoftwomethods:
comparisonoftoprank(COMP),andamethodusingLogisticRegression
(LR).
2.1FramebyFrameSoundClassication
UsingLDAFrame-by-framesoundclassicationwascarriedoutusingpatternmatchingoffeaturesextractedinthefrequencydo-main.Eachframerepresentsawindowon100msofrawaudiodata.Thesewindowsarethenjumpedovertheentiredatasetin25msincrements,producinga40Hzoutput.
Theaudiostreamwastakenatasamplerateof2kHzfromthewristwornmicrophone.FromthisaFastFourierTrans-form(FFT)wascarriedoutoneach100mswindow,generatinga100binoutputvector(12fsfftwnd=122100=100bins).
Makinguseofthefactthatourrecognitionproblemrequiresasmallnitenumberofclasses,weappliedLinearDiscriminantAnalysis(LDA)[1]toreducethedimensionalityoftheseFFTvectorsfrom100to#Classes1.
ClassicationofeachframecanthenbecarriedoutusingasimpleEuclideanminimumdistancecalculation.Wheneverwewishtomakeadecision,wesimplycalculatetheincomingpointinLDAspaceandnditsnearestclassmeanvaluefromthetrainingdataset.ThissavingincomputationcomplexitybydimensionalityreductioncomesatthecomparativelyminorcostofrequiringustocomputeandstoreasetofLDAclassmeanvaluesfromwhichtheLDAdistancesmightbeobtained.
Equally,anearestneighbourapproachmightbeused.Fortheexperimentdescribedherehowever,Euclideandistancewasfoundtobesucient.
Alargerwindow,wlen,wasmovedoverthedatainwjmpsecondincrements.Thisrelativelylargewindowwaschosentoreectthefactthatalloftheactivitiesweareinterestedinoccuratthetimescaleofatleastseveralseconds.OneachwindowwecomputeasumoftheconstituentLDAdistancesforeachclass.Fromthesetotaldistances,wethenrankeachclassaccordingtominimumdistance.Classicationofthewindowisthensimplyamatterofchoosingthetoprankingclass.
2.2HMMAccelerationClassication
Incontrasttotheapproachusedforsoundrecognition,weemployedmodelbasedclassication,specicallytheHiddenMarkovModel(HMM),forclassifyingaccelerometerdata[8,
11].(TheimplementationoftheHMMlearningandinferenceroutinesforthisexperimentwasprovidedcourtesyofKevinP.MurphyfiHMMToolboxformatlab[7].)
ThefeaturesusedtofeedtheHMMmodelswerecalculatedfromsliding100mswindowsonthex,y,andzaxisofthe100Hzsampledaccelerationdata.Thesewindowsweremovedoverthedatain25msincrements,producingthefollowingfeatures,outputat40Hz:
Meanofx-axis
Varianceofx-axis
Acountofthenumberofpeaks(forx,y,z)
Meanamplitudeofthepeaks(forx,y,z)
Finallywegloballystandardisedthefeaturessoastoavoidnumericalcomplicationswiththemodellearningalgorithmsinmatlab.
InpreviousworkweemployedsingleGaussianobservationmodels,butthiswasfoundtobeinadequateforsomeclassesunlessalargenumberofstateswereused.Intuitively,thedescriptivepowerofamixtureofGaussianismuchclosertofirealiythanonlyone,andsofortheseclassesamixturemodelwasused.Thespecicnumberofmixturesandthenumberofhiddenstatesusedwereindividuallytailoredbyhandforeachclass.Theparametersthemselvesweretrained
fromthedata.
Awindowofwlen,inwjmpincrements,wasrunovertheaccelerationfeatures,andthecorrespondingloglikelihoodforeachHMMclassmodelcalculated.
Classicationiscarriedoutforeachwindowbychoosingtheclasswhichproducesthelargestloglikelihoodgiventhestreamoffeaturedatafromthetestset.
2.3Fusionofclassiers
Comparisonoftopchoices(COMP)Thetoprankingsfromeachofthesoundandaccelerationclassiersforagivenjumpingwindowsegmentaretaken,compared,andreturnedasvalidiftheyagree.Thosewherebothclassiersdisagreearethrownout-classiedasnull.
Logisticregression(LR)Themainproblemwithadirectcomparisonoftopclassierrankingsisthatitfailstotakeintoaccountcaseswhereoneclassiermightbemorereliablethananotheratrecognisingparticularclasses.Ifoneclassierreliablydetectsaclass,buttheotherclassierfails
to,perhapsrelegatingtheclasstosecondorthirdran
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