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1、IEEETRANSACTIONSONBIOMEDICALCIRCUITSANDSYSTEMS,VOL.6,NO.2,APRIL2012167Energy-EfficientContextClassificationWithDynamicSensorControlLawrenceK.Au,StudentMember,IEEE,AlexA.T.Bui,Member,IEEE,MaximA.Batalin,Member,IEEE,andWilliamJ.Kaiser,SeniorMember,IEEEAbstract—Energyefficiencyhasbeenalongstandin
2、gdesignprocessingalgorithmsmustprovideadequateinformationthatchallengeforwearablesensorsystems.Itisespeciallycrucialdescribethepatients,butthewearablesystemmustbelight-incontinuoussubjectstatemonitoringduetotheongoingneedweightenoughtobeworninreallife.forcompactsizesandbettersensors.Thispap
3、erpresentsanAmongallthedesignrequirements,energyefficiencyhasenergy-efficientclassificationalgorithm,basedonpartiallyob-servableMarkovdecisionprocess(POMDP).Ineverytimestep,beenamajordesignconsiderationofwearableplatforms[10],POMDPdynamicallyselectssensorsforclassificationviaasensor[11].Battery
4、lifeisoftenseverelylimitedinwearableappli-selectionpolicy.Thesensorselectionproblemisformalizedascations,yetconstantclassificationandwirelesssensinginduceanoptimizationproblem,wheretheobjectiveistominimizesignificantenergyconsumption.Manyexistingapplicationsdomisclassificationcostgivensomeener
5、gybudget.Statetransitionsnotaddressthisproblemdirectlybecausethelengthoftheirex-aremodeledasahiddenMarkovmodel(HMM),andthecorre-spondingsensorselectionpolicyisrepresentedusingafinite-stateperimentsandpilotstudiesarerelativelyshortandthedepictedcontroller(FSC).Toevaluatethisframework,sensorda
6、taweresituationsareoftencontrolled.Suchscenariosmaynotbeap-collectedfrommultiplesubjectsintheirfree-livingconditions.plicableinpractice:batteryenergycanbecompletelydepletedRelativeaccuraciesandenergyreductionsfromtheproposedwithinhoursifnotmanagedproperly[12],andrechargingismethodarecompare
7、dagainstnaïveBayes(always-on)andsimpleoftenunavailablewhilethesystemsareinuse.Consequently,randomstrategiestovalidatetherelativeperformanceoftheal-gorithm.Whentheobjectiveistomaintainthesameclassificationmoreenergy-efficienttechniquesareneededtomanagebatte
