using non-intrusive environmental sensing for adls ... · itistheminimumactionrequiredfordailylife...

DOI: 10.4018/IJSI.2018100102

International Journal of Software InnovationVolume 6 • Issue 4 • October-December 2018

Copyright©2018,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.

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Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person HouseholdLong Niu, Graduate School of System Informatics, Kobe University, Kobe, Japan

Sachio Saiki, Graduate School of System Informatics, Kobe University, Kobe, Japan

Masahide Nakamura, Graduate School of System Informatics, Kobe University, Kobe, Japan & RIKEN AIP, Tokyo, Japan

ABSTRACT

Thisarticledescribeshowpervasivesensingtechnologiesarepromisingforincreasingone-personhousehold(OPH),whereasystemmonitorsandassistsaresidenttomaintainhealthyliferhythm.Automaticrecognitionofactivitiesofdailyliving(ADLS)hasbeenahotresearchtopicinpervasivecomputing.However,mostexistingmethodshavelimitationsindevelopmentcost,privacyexposure,and inconvenience for residents. To cope with the limitations, this article presents a new ADLrecognitionsystemespeciallyforOPH.Tominimizethedevelopmentcostaswellasintrusionstouserandhouse,thesystemexploitsanIoT-basedenvironmentsensingdevice,calledautonomoussensorbox(sensorbox)whichcanautonomouslymeasure7kindsofenvironmentattributes.Thesystemthenappliesmachine-learningtechniquestopredict7kindsofADLS.Finally,thisarticleconductsanexperimentwithinanactualapartmentofasingleuser.TheresultshowsthattheproposedsystemachievestheaverageaccuracyofADLSrecognitionwithabout88%,bycarefullydevelopingthefeaturesofenvironmentattributes.

KEywORDSActivities of Daily Living, ADLS Recognition, Feature Engineering, Machine Learning, Non-Intrusive Environment Sensing, One-Person Household

INTRODUCTION

Thegrowingnumberofunmarriedpeopleandlatemarriagesindevelopedcountriesleadstoasocialissueofone-personhousehold(OPH).InJapan,thenumberofOPHincreasingrapidly.Itisestimatedthat37.4%householdswillbecomeOPHin2030(MinistryofHealth,LabourandWelfare,2010).InsevenstatesofUSA,thepercentageofOPHexceeds30.3%in2015(Statista,2017).InChina,therearemorethan60millionpeoplecurrentlylivingalone(Yeung&Cheung,2015).AccordingtoAsaoka,Fukuda&Yamazaki(2004)andFujinoetal.(2006),peopleinOPHeasilylosecontrolhealthyliferhythm,sincenooneelsecantakecareofthelivinginOPH.Sincethelossofhealthyliferhythmoftenleadstohealthdeterioration,itisessentialtomaintaintheliferhythmespeciallyinthecontextofOPH.Ingeneral,aliferhythmischaracterizedbyactivitiesofdailyliving(ADLs,forshort).TypicalADLsinOPHincludeeating,takingbath,sleeping,etc.IfthecycleofADLs


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becomesverydifferentfromtheoneinahealthyliferhythm,theresidentislosinghis/herliferhythm.Tomaintaintheliferhythm,onehastokeeparegularrecordofADLs.However,keepingmanualrecordingrequiresstrongmindandpatience.

ToautomatetheADLrecordinginOPH,pervasivesensingtechnologiescombinedwithmachinelearningarequitepromising,becausetheycanrecognizeADLsfromautomaticallymeasureddata.TherehavebeenmanystudiesforADLrecognition.Someapproaches(e.g.,Fiore,Bodor,Drenner,Somasundaram&Papanikolopoulos,2008;Ouchi&Doi,2013)trytodirectlycapturethelivingusingcamera,ormicrophone.However,suchsystemsaretoointrusiveoftheuserinthesensethatthedailylivingisexposedasitis.Therearemanystudiesusingwearablesensors,and/orindoorpositioningsystemtorecognizeADLs(e.g.,Kusano,Muro,Hayashi,Harada&Shimakawa,2011;Peietal.,2013).However,thewearablesensorisintrusivetohumanbody,astheuseralwayshastowearthesensordeviceathome.Indeed,thehomeisaplacewheretheuserisfreefromtediousthings.Theindoorpositioningisintrusivetoahouse,inthesensethatsensorsandbeaconsmustbeinstalledintothehouseandobjects.Thisusuallycausesexpensivecostfordeploymentandmaintenance.

Toovercomethelimitations,weproposeanewsystemthatrecognizesADLsofOPHbasedonnon-intrusiveenvironmentalsensingwithmachinelearning.Intheproposedsystem,weexploitanIoT-basedenvironment-sensingdevice,calledautonomoussensorbox(wesimplycallSensorBox,hereinafter).SensorBoxhasbeendeveloped inourpreviouswork (Sakakibara,Saiki,Nakamura&Matsumoto,2016),andisdesignedtominimizetheeffortofdeploymentandoperation.Onceapowercableisconnected,SensorBoxautonomouslymeasuresseventypesofenvironmentattributes(temperature,humidity,light,sound,vibration,gaspressureandmotion)aroundthebox,andthenperiodicallyuploadsthedatatoacloudserver.Thus,alltheoperationsfordeploymentandmaintenanceareperformedwithouthumanintervention,orexpensiveinfrastructure.

AsSensorBoxismeasuring theenvironment inOPH, theproposedsystemalsorequires theinitialtraining,wheretheresidentmanuallyrecordsADLsusingadesignatedlifelogtool.Theinitialtrainingissupposedtobeperformedinseveraldays,toassociatelabelsofADLswiththesensordata.Intheproposedsystem,wedefinesevenbasicADLs(cooking,working,cleaning,takingbath,sleeping,eating,absence),whicharethemosttypicalADLsformaintainingtheliferhythm.Forthelabeleddataset,weapplysupervisedlearningalgorithmstoconstructamodelofADLrecognitionfor thehouse.For this,weperformcareful featureengineering todetermineessentialpredictorsthatwellexplainADLsinOPH.Furthermore,wetryseveraldifferentclassificationalgorithmstocomparetheperformance.

To evaluate the proposed system, we have deployed one SensorBox in an actual apartmentofasingleperson,andconductedanexperimentfortendays.ExperimentalresultsshowthattheaverageaccuracyofallthesevenADLswasaround87%withDecisionForestsupervisedlearning.TheaccuracyofsomespecificADLsachievedover90%.Fromthisresult,weconfirmedthattheproposedsystemachievesnon-intrusiveandpracticalADLrecognitioninOPH,usingSensorBox.

PRELIMINARy

Activities of Daily Living (ADL)ADLisaprofessionalwordoriginallyusedathospital.Itistheminimumactionrequiredfordailylifesuchassleeping,meal,takingbath,etc.itisusedasanindicatoroftheaginganddegreeofdisability.ThediscoveryandrecognitionofADLisanessentialfunctionofthesystemthatprovidesnecessaryassistanttotheresidentsofOPH.Basedontheresultsofthisprocess,theintelligentsystemcandecidewhichactiontotakeinordertosupporttheresidents’well-beingandunderstandresidents’liferhythmbasedontheregularrecordofADLs.


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ADLs RecognitionSincetheneedofADLrecognitionisgreat,researchershavebeenstudyinganddevelopinganumberofmethodologiestotacklethisproblem.TheapproachestotheADLrecognitioncanbedividedroughlyintotwocategories,dependingonthetypeofcontextual informationanalyzed.Thefirstcategoryusesmultimediadatatakenbyvideocamerasormicrophonerecordings,tocapturethedailylivingdirectly.Thesecondcategoryusestime-seriesdatameasuredbyvarioussensors,includingaccelerometer,gyroscope,RFIDandpower-meterssensors.

Multimedia DataBrdiczka,Reignier&Crowley(2007)proposedasmarthomethattakesvideosofresidents,andprocessesthevideotorecognizeactivities.Althoughgeneralpeoplehavebeenresistedtotheat-homevideomonitoring(Hensel,Demiris&Courtney,2006),theacceptanceofthistechnologyinthehomeisincreasing.Ontheotherhand,processingthevideoiscomputationallyexpensive.Itreliesuponthefirsttrackingoftheresidentbeforethecorrectvideodatacanbecapturedandanalyzed.

Sensor DataSincetakingvideoandaudioexposestoomuchinformationofdailyliving,itisconsideredtobeintrusivetothelife.Therefore,itismoreappreciatedtousepassiveinformation.Hence,mostofthecurrentresearchinADLsrecognitionusesensordata.Researchershavefoundthatcombiningdifferenttypesofsensoriseffectiveforclassifyingdifferenttypesofactivities.

Kusanoetal.(2011)proposedasystemthatderivesliferhythmfromtrackingelderlymovementbyusingRFIDpositioningtechnology.TheyinstallmanyRFIDreadersonthefloorofahouse,andaskparticipantstowearslipperswithRFIDtags.Thereaderscaptureindoorlocationofresident.Thesystemreasonstheliferhythmoftheuserfromthetime-serieslocationdata.However, it isdifficult todeterminetheexactactivityusingthemovementhistory.Asaresult, theaccuracyofADLsrecognitionislow.

Tapia,Intille&Larson(2004)focusedoninteractionsofaresidentwithanobjectofinterestsuchasadoor,awindow,arefrigerator,akeyandamedicinecontainer.Theyinstalledstate-changesensorsondailyitemstocollecttheinteractiondata.

Philiposeetal. (2004)attachedanRFIDtagonevery item,andaskedaparticipant toweargloveswithanRFIDtagreader.Whentheparticipantisclosetotheitem,theinteractionisrecorded.

Peietal.(2013)combinedapositioningsystemandmotionsensorofasmartphonetorecognizehumanmovementsinnaturalenvironments.However,whenturningonthemotion-sensor,Wi-FiandGPSsimultaneously,thebatterydrainisveryhigh.Anotherproblemisthatausermaynotwanttocarrysmartphonealltimeathome,whichisthelimitationofcollectingdata.

SensorBoxSensorBoxisanIoTdevicewithmultipleenvironmentsensors,developedbyourresearchgroup(Sakakibaraetal.,2016).Itcanmeasuresevenenvironmentalattributesaroundthebox,whicharetemperature,humidity,lightingintensity,atmospherepressure,soundvolume,humanmotionandvibration.Itwasdesignedtominimizethecost-intensiveinfrastructureandconfigurationlabor.Onceconnectedtopowerandnetwork,SensorBoxautonomouslymeasuresenvironmentattributearoundtheboxanduploadsdetecteddatatocloudserver.

Challenges and Research GoalTheADLrecognitionhasbeenwidelystudiedforfewyears.BykeepingtrackofADLs,asmartpervasivesystemcanprovideremindersforresidents,aswellasreacttohazardoussituations(Wren&Tapia,2006).Mostofthesestudiesapplytoelderlypeople,cancerpatients,andordinaryfamilies.However,therearenotsomanystudiesforOne-PersonHousehold(OPH).Theuniquecharacteristics


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ofOPHaretheresidentislivingalone,andisoftenbusytodoeverythingbyoneself.He/shedoesnotwanttochangetheownwayofliving,orpayforexpensivesystemjustformonitoringADLs.

AsmentionedinSection2,therearemanyexistingsystemsthatusewearablesensors,object-embeddedsensors,orindoorpositioningsystems.However,weconsideritdifferentforpeopleinOPHtoacceptthesetechnologies,becausetheyaretooexaggeratedandintrusivefortheirlife.Wecaneasilyimaginethatmostresidentswillforgetorgiveupwearingthesensor,sincethehomeistheplacewheretheresidentmakeoneselfcomfortable.Althoughlabsorcompaniescanmanagethelarge-scaleequipment,itisstilltooexpensivetodeployinOPH.

Ourresearchgoalistominimizesuchlimitationsoftheconventionalapproaches,andtoachievehigh-qualityADLrecognitionofOPH.

OUTLINE OF PROPOSED SySTEM

In order to achieve the research goal, we propose a new ADL recognition system for OPH. Tominimizetheintrusionsandthecost,theproposedsystemjustreliesontheenvironmentalsensingbytheautonomoussensorbox(SensorBox)(Sakakibaraetal.,2016).Figure1showsthearchitectureoftheproposedsystem.Usingthefigure,weexplaintheproposedsystemfromlefttoright.

First,wesetupthesystemwithinatargetOPH.Wedeployasingle(ormultipleifnecessary)SensorBoxinapositionwhereADLsarewellobservedasenvironmentmeasures.Wealsoinstallasoftware,calledLifeLogger,onuser’sPC.Toapplysupervisedmachine-learningalgorithms,theproposedsystemrequirestrainingdataattheinitialphaseofoperation.Forthis,LifeLoggerisusedtoattachcorrectlabelsofADLs(aslifelog)totheenvironmentsensingdata.

Then,thesystembeginstocollecttime-seriesdata.SensorBoxuploadsthemeasureddatatoMongoDBinacloudserver,whereasLifeLoggerinsertsthelifelogintoMySQLintheclouddata.

Finally,thesystemjoinsthetwotime-seriesdatawiththetimestamptoformthetrainingdata.WeapplymachinelearningtothetrainingdatatoconstructapredictionmodelofADLrecognition.

DATA COLLECTION

Environment SensingTobeabletodetectADLbyanalyzingnon-intrusiveenvironmentattributes,thetargetattributesmustbesensitivetothechangingofresident’sADL.ConsideringthattherangeofsensibleisonlyaroundtheSensorBox,theboxshouldbeputonwhereresident’sADLisfrequentlyconducted.However,thelayoutofeachhouseandlivingstayofeachsinglearedifferentthingsfromOPHtoOPH.HencethemostsuitablepositionofSensorBoxisalsodifferentfromOPHtoOPH.

Figure 1. Architecture of proposed system


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SensorBoxmeasures sevenenvironmentattribute,whichare temperature,humidity, lightingintensity,atmospherepressure,soundvolume,humanmotionandvibration,inevery10seconds.Figure2showsthescreenshotoftherawsensorsdatathatbemodifiedtoJSONformattext.AndFigure3showsthescreenshotofanapplication,whichvisualizescollectedrawsensorsdataoncloudservice.ThefirstgraphofFigure3showsthechangingofbrightnesswithtime.Andwecaneasilyfindthatthosechangingisman-made.

Activity LabelingDuringinitialseveraldays,theresidentneedstoinputcorrectlabelsofADLs,sothatthesystemcanlearntheADLsfromtheenvironmentsensingdata.Forthis,weasktheresidenttouseLifeLogger.Figure 4 shows the user interface of LifeLogger. As shown in the figure, LifeLogger has eightbuttons,eachofwhichcorrespondstoanADL.WhentheresidentstartsanADL,he/shejustpressesthecorrespondingbuttontorecordthecurrentADL.Basedontherelevantstudies(Fujinoetal.,2006;Fioreetal.,2008),wehavechoseneighttypesoftypicalADLs(sleep,eat,cook,workingat

Figure 2. Raw sensors data

Figure 3. Sensor data visualization


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PC,clean,bath,absenceandother),andregisteredtheminLifeLogger.Whentheresidentpressesabutton,thesystemrecordsthelabel,andstoresitinMySQLinacloudserver.

Integrate Environment Sensing and Activity Labeling DataForsupervisedlearning,thesystemneedstotrainingdatawhichhavethecorrespondencebetweentheADLsanddatainadvance.Inordertoestablishtrainingdata,weintegratethetwotime-seriesdatacollectedbySensorBoxandLifeLoggerbyjoiningbasedontimestamp.Sincedatalabelledas‘other’wasbeyondthescopeoftheADLrecognition,thosenoisedatamustbefiltered.Table1showsthetrainingdata.

ESTABLISH MACHINE LEARNING RECOGNITION MODEL

Analysis Activity-sensitive Environment Sensing SensorsForaccurateADLrecognition,itisessentialtoidentifywhatenvironmentalvaluesinthesensingdatawellpredicttheADL.FromthesevenenvironmentalattributesofSensorBox,weonlychoose

Table 1. Training data

DateTime vibration light motion gaspressure temerature Humidity sound activityID

2017/2/193:33:02 495 1 0 98.8 13.33 35.84 50.15 5

2017/2/193:33:12 494 1 0 98.8 13.33 36.04 0 5

2017/2/193:33:22 494 1 0 98.8 13.33 36.04 51.62 5

2017/2/193:33:32 494 1 0 98.8 13.33 36.04 0 5

Figure 4. Lifelogger tool


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temperature,humidity,lightingintensity,soundvolumeandhumanmotion,sincetherestattributes(vibration and atmosphere pressure) seem irrelevant to the target ADLs (i.e., sleep, eat, cook,workingatPC,clean,bath,absenceandother).Forexample,fromthefourgraphsofFigure3showsthechangingoffourenvironmentattributesinoneday,wecanseeonlythegraphofgasPressureisasmoothcurve.AccordingtothisfigurewemakejudgementsthatthegasPressureisalmostnotaffectedbytheresident’sADL.

Feature EngineeringFeaturevalueisdatathatiseffectivetoidentifytheADLs.Inourstudy,wegetthefeaturevaluefromtrainingdata,asthefollowingprocess.

First,wedeterminethesizeoftime-window.Toenhancethefeatureofthetime-seriesdata,weaggregatetherawdatawithinthesametime-windowintoonedata.Forthis,thewindowsizeaffectstheaccuracy.Ifthesizeistoolarge,thewindowislikelytocontaindifferentactivities.Iftoosmall,thewindowwillnotcontainsufficientdatatoreasonandpredictanADL.Hence,wetest3variationsof1,2and3minutes.InordertofacilitatethediscussioninSection4,weusesymbols(‘A’,‘B’,‘C’)topresentdifferentdatasetswithdifferentsizeoftime-window.ThedetailisA:1minute,B:2minutesandC:3minutes.

Finally,foreachofthefiveenvironmentattributeschosen,wedetermineanaggregationfunction.Anaggregationfunctionaggregatesallthedatawithinthesametime-window.TypicalaggregationfunctionincludesMAX,MIN,AVG,STDEV,andsoon.Basedonthenatureofeachenvironmentattribute,wecarefullychooseanappropriatefunction.Weneedtoapplydifferentaggregationfunctiontoeachenvironmentattribute.Byanalyzingall thetest,wewillfindtheoptimalcombinationofaggregationfunctionfromtest.However,ifwewanttotestallsituations,weneedtoconducthundredsroundsoftest,whichwilltakealotoftime.Toeffectivelytestsallcasesoffunctioncombination,weusedatoolcalledPICT(Jacek,2009).PICTgeneratesacompactsetofparametervaluechoicethatrepresentthetestcasesyoushouldusetogetcomprehensivecombinatorialcoverageofyourparameters.Table2showsthe9casesofcombinationgeneratedbyPICT.

Establish Recognition ModelForthedevelopedfeaturesofthetrainingdata,weapplymachine-learningalgorithm,inordertoconstructapredictmodelforADLrecognition.Weusepopularclassificationalgorithms,includingLogistic Regression, Decision Forest and Neural Network. By using these algorithms, we haveconstructedpredictionmodelsthatclassifiesgivenenvironmentsensordataintooneofthesevenADLs.

Table 2. Nine groups of aggregation function

Group light motion temperature humidity sound

G1 MIN MAX AVE AVE AVE

G2 MAX MAX STD STD STD

G3 AVE AVE STD STD MAX

G4 MAX AVE AVE AVE MAX

G5 MIN AVE AVE STD AVE

G6 AVE AVE AVE AVE STD

G7 MAX MAX STD AVE AVE

G8 AVE MAX AVE AVE AVE

G9 MIN AVE STD STD STD


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EVALUATE OF EXPERIMENT

Experiment SetupWedeployedtheproposedsysteminarealapartmentofasingleresident.AsshowninFigure5,theapartmentisanordinaryapartmentinJapan,consistingofabed/livingroom,abathroomandakitchen.WehaveplacedonesingleSensorBoxintheKitchenroom,sothatSensorBoxcanobserveADLsofresidentwell.ThepositionofSensorBoxismarkedbyaredpininFigure5.Atotalof45,693rowsoflabeledsensordata,whichdonotincludedatalabeledwith‘other’,becollectedduring10dayswithintheapartment.

ResultWehaveestablished81recognitionmodelsbasedonthe3sizeoftime-windows,9combinationsofAggregationFunctionsand3machine-learningalgorithms,andtestedthosemodelsbytrainingandlearningthecollecteddata.Inthissubsection,weshowthetestresultofallmodels,theAverageAccuracyofeachtrainedADLsrecognitionmodel.Accuracymeasuresthegoodnessofaclassificationmodelastheproportionoftrueresulttototalcases.Averageaccuracywhichistheaverageofeachaccuracyperclass(sumofaccuracyforeachclasspredicted/numberofclass).

Inordertofacilitatetheobservation,wedivideallthemodelsintothreetablesbythesizeoftime-window,Table3,4and5,anddrawabargraphforeachtable,Figure6,7and8.

Forthetables,eachcolumnstandsforthemethodofdataprocessinginfeatureengineeringandisidentifiedbyanamethatcontains2characters,acapitalletterandanumber.Thecapitallettermeans the size of time-windows, as mentioned in subsection Feature Engineering. The numbermeansthecombinationofaggregationfunctions’groupnumberinTable2.Inoneexample,inTable3,A4 stands for the sizeof time-window is1minutesandutilizedcombinationofAggregationFunctionsisG4onthedataprocessoffeatureengineering.Therowoftableisidentifiedbythenameofmachine-learningalgorithm.Eachcellshowstheaverageaccuracyofeachmodel.Inoneexample,therecognitionmodel’saverageaccuracyexceeded88.10%inthesituationwherethesizeoftime-windowis3minutes,utilizedcombinationofaggregationfunctionisG4andalgorithmisthemulticlassdecisionforest.

Forthethreebargraphs,theverticalaxisisaverageaccuracyandthelateralaxisstandsforthecolumnofrelevanttable.Thecolorofbarstandsforthealgorithms,therowofrelevanttable.

Bycomparingtheaverageaccuracyofmodelsondifferentsizeoftime-window,suchasthebluebarsofA1B1andC1,wecanseethatthesizeoftime-windowhasslightlyinfluenceontheaverageaccuracyofmodel.Bycomparingtheresultofmodelsondifferentmethodsoffeatureengineeringinonegraph,wecanseethatthemodelshavesignificantlydifferentperformancewiththecombination

Figure 5. Testbed apartment


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Figure 6. Visualization of Table 3

Table 3. All results of time-windows on 1 minute

Multiclass Algorithm A1 A2 A3 A4 A5 A6 A7 A8 A9

NeuralNetwork 84.36% 83.98% 84.27% 83.95% 83.96% 84.61% 84.28% 85.27% 84.23%

DecisionForest 85.83% 86.56% 86.24% 83.91% 84.43% 86.78% 87.54% 87.83% 86.41%

LogisticRegression 83.51% 85.23% 85.21% 83.16% 85.73% 83.18% 85.92% 82.05% 85.22%


Multiclass Algorithm B1 B2 B3 B4 B5 B6 B7 B8 B9

NeuralNetwork 84.10% 84.92% 84.87% 85.14% 83.08% 85.66% 84.45% 85.14% 85.09%

DecisionForest 84.52% 87.68% 86.18% 87.56% 84.62% 84.59% 87.30% 83.72% 86.49%



Multiclass Algorithm C1 C2 C3 C4 C5 C6 C7 C8 C9

NeuralNetwork 84.62% 85.14% 85.07% 84.99% 82.11% 85.44% 84.25% 84.29% 84.70%

DecisionForest 85.92% 86.44% 85.10% 88.10% 85.25% 84.62% 87.10% 84.66% 86.21%



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ofaggregationfunctions.Byobservingthethreegraphoftable,Figure6,7,8,wecanseethemodelsutilizedmulticlassdecisionforest,representedbyorangebars,havebetterperformancethanothermodels.

EvaluationInthesubsection,weevaluatetherelationshipbetweenthe3factorsandaccuracyofrecognitionADLsbasedonthepartofrepresentativedatawhichisselectedfromhugevolumesofexperimentaldata.

First,theeffectoftime-windowonaccuracy.Table6showsthreerecognitionmodels’accuracyof some ADLs (cooking, sleeping, and eating). The three recognition models are utilized same




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aggregationfunctionandalgorithmexpectthesizeoftime-window.Fromtheresults,wecanseethattheaccuracyofthreeADLsisslightlyaffectedbythesizeoftime-window,andthe2minutesislikelyanappropriatevaluebecauseaccuracyofrecognitionADLsishigherwhenthesizeoftime-windowis2minutes,expectrecognitioneating.Althoughthechangeofaccuracyisalsoslightlycauseofthechangeofsizeoftime-windowsisverysmall,theresultconfirmstheviewoftheabove-mentionedthatthesizeoftime-windowshouldnottoolargeandtoosmall.

Second,theeffectofaggregationfunctiononaccuracy.Table7showsthreerecognitionmodels’accuracy of recognition some ADLs (cleaning, sleeping, and going out). The three recognitionmodelsutilizedthesamesizeoftime-windowandalgorithmexceptthecombinationofaggregationfunctionforthefiveenvironmentattributes.Fromtheresult,wecanseeaggregationfunctionhasagreatinfluenceontheaccuracyofeachADLsrecognition.AsfortheG4,theaccuracyofgoingoutrecognitionisonly17.6%,whichis45%lessthanG8,buttheaccuracyofpredictsleepingisalmostequaltoG8.Forthethreemodels,theaccuracyofsleepingrecognitionachievesthehighestvaluewithusingthecombinationofG7.ButthemodelofG7performsbadlyonrecognitioncleaningandgoingout.ForG8,theaccuracyofpredictsleepingislessthanG7,buttheperformanceofpredictcleaningandgoingoutismuchbetterthanG7.Hence,fromthecompareswecanseethatsystemneedtoapplydifferentcombinationsoffunctiontoeachADLs,inotherwords,thefeaturevalueofADLsisdifferentwitheachother.

Last,theeffectofalgorithmsonaccuracy.Table8showsthreerecognitionmodels’accuracyofrecognitionsomeADLs(cooking,sleeping,andeating).Thosemodelsutilizedthesamesizeoftime-windowandcombinationofaggregationfunctionsexceptalgorithms.Fromtheresults,wecanseethatDecisionForestperformsbetterthanLogisticRegressionontherecognitionthreeADLs.FortheNeuralNetwork,ithasthebestperformanceonpredictsleepingandeating,butithasworstperformanceonpredictcooking.Forthereasonofabnormalresult,wethinkthatthisamountofdata

Table 6. Compare the accuracy of three models on different time-windows

Time windows Cooking Sleeping Eating

1minute(A) 86.00% 89.90% 54.10%

2minutes(B) 89.70% 89.90% 55.70%

3minutes(C) 87.80% 89.70f% 55.90%

Table 7. Compare the accuracy of three models on different aggregate functions

Aggregate Function Cleaning Sleeping Absence

G4 47.10% 73.00% 17.60%

G7 39.40% 95.50% 21.10%

G8 62.60% 72.70% 62.40%

Table 8. Compare the accuracy of three models on different algorithms

Multiclass Algorithm Cooking Sleeping Eating

LogisticRegression(LR) 59.60% 44.30% 41.30%

DecisionForest(DF) 67.50% 72.70% 62.60%

NeuralNetwork(NN) 20.20% 85.30% 93.50%


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isnotsuitableforneuralnetwork.Becausetheamountofcookingandeating’sdataisonly3.8%and5.2%ofthetotaldata,lessthan400elements.Forthesethreemodels,themodelofDecisionForestperformsmorerobustthanotherswiththelimitednumberoftrainingdata.

CONCLUSION

Inthispaper,wehaveproposedanewsystemthatautomaticallyrecognizesADLinOPH.ConsideringthecharacteristicsofOPH,theproposedsystemexploitsonlyenvironmentalsensingbySensorBox.Thisminimizesthecostofdeployment,aswellastheintrusiontotheresidentandthehouse.Toevaluatetheproposesystem,wedeployedthesysteminanactualapartmentofasingleresident,andcollectedsensordataandlifelog(ascorrectlabels)for10days.Throughsupervisedlearningwithcareful featureengineering,wewereable toconstructpractically feasiblemodelsofseven typesofADLs.TheaverageaccuracyofallADLsachievedmore than88%.Forsleepingrecognition,theaccuracyofrecognitionachievedmorethan90%.Andtheinfluenceofsizeoftime-windows,aggregationfunctionandmachine-learningalgorithmsontheaccuracyofrecognitionADLs.

As for the futurework,weevaluate theproposedsystem inmultiplehouses to seehow thelearningprocessesvariesfromonehousetoanother.Moreover,wewanttovalidateiftheproposedeighttypesofADLsareenoughtocapturetheliferhythmsinOPH.Finally,developingservicesthatactuallyassisthealthyliferhythmusingtherecognizedADLsisourlong-termgoal.

ACKNOwLEDGMENT

ThisresearchwaspartiallysupportedbytheJapanMinistryofEducation,Science,Sports,andCulture[Grant-in-AidforScientificResearch(B)(No.16H02908,No.15H02701),ChallengingExploratoryResearch(15K12020)],andTateishiScienceandTechnologyFoundation(C)(No.2177004).


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Long Niu received a B.E. Degree in Information engineering from Zhengzhou University, China, in 2012, a M.E. Degree in Computational Science from Kobe University, Japan, in 2016. Now he has a Ph.D. candidate in the Graduate School of System Informatics at Kobe University. His research interests include Activity Recognition by Using Non-intrusive Environment Sensing Data and Integration Framework for Indoor Location.

Sachio Saiki is an assistant professor in the Graduate School of System Informatics at Kobe University. His research interests include software engineering education and signal processing.

Masahide Nakamura received B.E., M.E., and Ph.D. degrees in Information and Computer Sciences from Osaka University, Japan, in 1994, 1996, 1999, respectively. From 1999 to 2000, he has been a post-doctoral fellow in SITE at University of Ottawa, Canada. He joined Cybermedia Center at Osaka University from 2000 to 2002. From 2002 to 2007, he worked for the Graduate School of Information Science at Nara Institute of Science and Technology, Japan. He is currently an associate professor in the Graduate School of System Informatics at Kobe University. In 2015, he worked for Universite Grenoble Alpes as a visiting professor. In 2018, he joined RIKEN Center for Advanced Intelligence Project (AIP)

as a visiting researcher. His research interests include the service/cloud computing, smart home, smart city, and gerontechnology. He is a member of the IEEE, IEICE and IPSJ.

using non-intrusive environmental sensing for adls ... · itistheminimumactionrequiredfordailylife...

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