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1、1166IEEESENSORSJOURNAL,VOL.12,NO.5,MAY2012MEMSAccelerometerBasedNonspecific-UserHandGestureRecognitionRuizeXu,ShengliZhou,andWenJ.Li,Fellow,IEEEAbstract—Thispaperpresentsthreedifferentgesturerecogni-recognitionsystemwhichcanrecognizesevenhandgesturesintionmodelswhicha
2、recapableofrecognizingsevenhandgestures,3-Dspaceisbuilt.Thesystemhaspotentialusessuchasare-i.e.,up,down,left,right,tick,circle,andcross,basedontheinputmotecontrollerforvisualandaudioequipment,orasacontrolsignalsfromMEMS3-axesaccelerometers.Theaccelerationsofahandinmo
3、tioninthreeperpendiculardirectionsaredetectedmechanismtocommandmachinesandintelligentsystemsinof-bythreeaccelerometersrespectivelyandtransmittedtoaPCviaficesandfactories.Bluetoothwirelessprotocol.Anautomaticgesturesegmentational-Manykindsofexistingdevicescancaptureges
4、tures,suchasgorithmisdevelopedtoidentifyindividualgesturesinasequence.Tocompressdataandtominimizetheinfluenceofvariationsre-a“Wiimote,”joystick,trackballandtouchtablet.Someofthemsultedfromgesturesmadebydifferentusers,abasicfeaturebasedcanalsobeemployedtoprovideinputto
5、agesturerecognizer.onsignsequenceofgestureaccelerationisextracted.ThismethodButsometimes,thetechnologyemployedforcapturinggesturesreduceshundredsofdatavaluesofasinglegesturetoagesturecanberelativelyexpensive,suchasavisionsystemoradatacodeof8numbers.Finally,thegesture
6、isrecognizedbycomparingthegesturecodewiththestoredtemplates.Resultsbasedon72ex-glove[8].Tostrikeabalancebetweenaccuracyofcollecteddataperiments,eachcontainingasequenceofhandgestures(totalingandcostofdevices,aMicroInertialMeasurementUnit628gestures),showthatthebestoft
7、hethreemodelsdiscussedisutilizedinthisprojecttodetecttheaccelerationsofhandmo-inthispaperachievesanoverallrecognitionaccuracyof95.6%,withthecorrectrecognitionaccuracyofeachgesturerangingfromtionsinthreedimensions.91%to100%.WeconcludethatarecognitionalgorithmbasedonTh
8、erearemainlytwoexistingtypesofgesturerecognitionsignsequenceandtemplatematchingaspresentedinthispapercanmethods,i.e.,vision-basedan
