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Reiner Wichert

Beate Mand Editors

Ambient Assisted Living

9. AAL-Kongress, Frankfurt/M, Germany, April 20–21, 2016

AdvancedTechnologiesandSocietalChange

Moreinformationaboutthisseriesathttp://www.springer.com/series/10038

9.AAL-Kongress,Frankfurt/M,Germany,

April20–21,2016

Editors

ReinerWichert

SageLivingGmbH

Pfungstadt Germany

BeateMand VerbandderElektrotechnikElektronik Informationstechnike.V. Frankfurt Germany

ISSN2191-6853ISSN2191-6861(electronic) AdvancedTechnologiesandSocietalChange

ISBN978-3-319-52321-7ISBN978-3-319-52322-4(eBook) DOI10.1007/978-3-319-52322-4

LibraryofCongressControlNumber:2016963785

©SpringerInternationalPublishingAG2017

Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart ofthematerialisconcerned,speciﬁcallytherightsoftranslation,reprinting,reuseofillustrations, recitation,broadcasting,reproductiononmicroﬁlmsorinanyotherphysicalway,andtransmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilaror dissimilarmethodologynowknownorhereafterdeveloped.

Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthis publicationdoesnotimply,evenintheabsenceofaspeciﬁcstatement,thatsuchnamesareexempt fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.

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Printedonacid-freepaper

ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland

Preface

Thehabitatsoftomorrowwillbeconnectedthroughnetworked,autonomous,and assistivesystems.Houses,apartments,ofﬁces,transport,orpublicspacesare convertingintohealthplaces.Assistancetechnologiesenableasmoothtransition fromcomfortablehealthsupporttomedicalornursingcare.Toenablethis,Active AssistedLiving(AAL)combinesawholerangeofinnovativekeytechnologies fromthesedomains.Nowadays,wecanrecognizethetrendthatpreciselythis communityisdiscussingdomainspanningsystemconceptstointegrateseamlessly andspontaneouslythevariouscomponentsandsolutionsintoanoverallsystem approach.

Whilethispotentialhasbeenrecognizedforsometime,breakthroughsinterms ofwidespreadavailabilityanddeploymentofsolutionshaveyettobeachieved. TheEUandtheAALAssociationhavefundedactivitiesinthisareaforsomeyears, andsomeofthesearenowatastageintheirdevelopmentwheredirecthands-on involvementofdevelopmentcompaniesisthebestwaytomakesurethatthiswork producesresultsthatareeffectiveandapplicableinrealindustrialsettings.

Tofollowthesegoals,aconferenceserieshasbeenestablishedasanannual showcaseeventforthepeopleinvolvedinthiscommunity:theAAL-Kongress (CongressforActiveAssistedLiving)withitspurposeistoexhibitanddemonstrate ICTsolutions,promotenetworkingwithinthecommunity,provokedebateon varioustopicsandhighlightneworemergingdevelopmentsintheareatoinform theAALcommunityanddiscusstheproblemsandchallengeswehavetofacein thecommonyears.

The ﬁrstAALKongress2008hadthefocusonapplicationsofintelligent assistivesystemswithintheareasof “health&homecare” , “safety&privacy” , “maintenance&housework” and “socialenvironment”,Atthesecond AAL-Kongress,morethan520participantsattended.Itfocusedonusecasesto supportthemanufacturingofproductsadjustedtotheneedsoftheuser.In2010the thirdAAL-Kongresshadbeenorganizedwithcloseto600participantsalsowith thefocusonusecases.In2011,itadvancedtotheleadingcongressforAALwith 870participants.In2012,thefocuswaslaidontechnologiesinaself-determined lifeandthenumberofparticipantspassedover1000,stilladdressingeconomic

challengesandtrendsettingapplicationsoninnovativetechnology.In2013,the sixthAAL-Kongresswasfocussingon “qualityoflifeintimesofchanging demographyandtechnology”.Withinthethematictopic “BetterLifewithAssistive Technologies” thecongressaddressedin2014thebasichumanneedsinthedifferentareasofhousing,mobility,work,healthandcare.In2015,theformatofthe conferenceserieshasbeenchangedbycombiningtheAAL-Kongresswiththefair “ZukunftLebensräume” tobringAALclosertothepeople.

In2016,theseriescontinuedinthecombinedformatandwith750participants thecongresswasagainagreatsuccesswithitsexcellentplatformtoexchange knowledgebetweenallstakeholders,fromdevelopers,manufacturersandusers, serviceproviders,endusersandrepresentativesfrompolitics,industryandassociations.Thistimethecongressfocussedontechnologyforassistanceinhealth, independenceandcomfort.Closeto500authorsfromsixdifferentcountrieshad submittedcontributionswhere108papershadbeenaccepted.Afterasolidreview process,14paperswereacceptedtobeincludedinthesescienti ﬁcproceedings oftheconference.Threeindependentreviewerswerematchedbytheirexpertise areatothetopicofeachpaper.

Inclosing,Iwouldliketothankthe36reviewersoftheReviewingCommittee, alltheauthors,theorganizersofthiseventandtheconferenceparticipantswho helpedtomakethiscongressasuccess.

ReinerWichert ChairofScientiﬁcProgramCommittee

OrganizingCommittee

ChairScientiﬁcProgramCommittee

ReinerWichert,SageLivingGmbH

ScientiﬁcProgramCommittee/ReviewCommittee9.

AALKongress “ZukunftLebensräumeKongress2016”

JanAlexandersson,DFKISaarbrücken

RashidAsarnusch,ZentrumfürTelemedizinBadKissingen

SergeAutexier,DFKIBremen

DanielBieber,InstitutfürSozialforschungundSozialwirtschaft

MichaelBrach,UniversitätMünster

MartinBraecklein,LindeHealthcare

AndreasBraun,FraunhoferIGD

AlexandraBrylok,VSWG

WolfgangDeiters,FraunhoferISST

MarcoEichelberg,Ofﬁs

UweFachinger,UniversitätVechta

MelinaFrenken,JadeHochschule

PetraFriedrich,HSKempten

BirgitGraf,FraunhoferIPA

IngridHastedt,WohlfahrtswerkBaden-Württemberg

AndreasHein,UniversitätOldenburg

SvenjaHelten,UniversitätVechta

OliverKoch,HochschuleRuhrWest

BennoKotterba,iAQInstitutfürAssistenzsystemeundQualiﬁzierunge.V.

PetraKnaup-Gregori,UniversitätHeidelberg

HaraldKünemund,UniversitätVechta

JaninaLaurila-Dürsch,DKE

SybilleMeyer,SIBISInstitut

HeidrunMollenkopf,BAGSO

StephanieNobis,UniversitätVechta

ChristinaRode-Schubert,TCI

LotharSchoepe,SmartLivingGmbH

GudrunStockmanns,HochschuleRuhrWest

UweTronnier,FHKaiserslautern

FrankWallhoff,JadeHochschuleOldenburg

ClausWedemeier,GdW

ChristineWeiß,VDI/VDE-IT

VolkerWittpahl,WittpahlIngenieurs-undInnovationsbüro

WolfgangZagler,TechnischeUniversitätWien

AntonZahneisen,SOPHIAlivingnetworkGmbH

PartITechnicalAssistanceforUrbanAreas

IntegrationofStationaryandWearableSupportServices foranActivelyAssistedLivingofElderlyPeople:Capabilities, Achievements,Limitations,Prospects ACaseStudy 3 RainerLutzeandKlemensWaldhör

QuoVadis DeﬁnitionofRequirementsandConception forInterconnectedLivinginaQuarterforDementiaPatients ........

AlexanderGerka,NadineAbmeier,Marie-LuiseSchwarz, StefanieBrinkmann-Gerdes,MarcoEichelbergandAndreasHein

PartIITechnologyforSmartEnvironments

InvisibleHumanSensinginSmartLivingEnvironments UsingCapacitiveSensors

43 AndreasBraun,SilviaRusandMartinMajewski

LivingCare AnAutonomouslyLearning,HumanCentered HomeAutomationSystem:CollectionandPreliminaryAnalysis ofaLargeDatasetofRealLivingSituations 55 RalfEckert,SebastianMüller,SebastianGlende,AlexanderGerka, AndreasHeinandRalphWelge

NewApproachesforLocalizationandActivitySensing inSmartEnvironments ........................................

73 FlorianKirchbuchner,BiyingFu,AndreasBraun andJulianvonWilmsdorff

TechnologySupportedGeriatricAssessment ......................

85 SandraHellmers,SebastianFudickar,ClemensBüse,LenaDasenbrock, AndreaHeinks,JürgenM.BauerandAndreasHein

PartIIITechnologytoSupportMobility

GestureControlledHospitalBedsforHomeCare .................. 103

S.Fudickar,J.Flessner,N.Volkening,E.-E.Steen,M.IskenandA.Hein

AssistedMotionControlinTherapyEnvironmentsUsingSmart SensorTechnology:ChallengesandOpportunities 119

JuliaRichter,ChristianWiede,AndréApitzsch,NicoNitzsche, ChristianeLösch,MartinWeigert,ThomasKronfeld,StefanWeisleder andGangolfHirtz

eNav:ASuitableNavigationSystemfortheDisabled 133 DženanDžaﬁć,PierreSchoonbrood,DominikFranke andStefanKowalewski

PartIVTechnicalResearchforResideandLiving

RegulationofVentilationSystemsBasedonPsychophysical Principles ...................................................

J.FlessnerandM.Frenken

Computer-BasedAdaptionofCookingRecipesIntegrated inaSpeechDialogueAssistanceSystem

163 KarenInsaWolf,StefanGoetzeandFrankWallhoff

LearningBehaviouralRoutinesforEarlyDetection ofHealthChanges 173

RaoulHoffmann,AxelSteinhageandChristlLauterbach

EnablinganInternetofThingsFrameworkforAmbient AssistedLiving 181 HelmiBenHmidaandAndreasBraun

StandardisationforMobility-RelatedAssistedLivingSolutions: FromProblemAnalysistoaGenericMobilityModel ............... 197 MichaelBrach,ArminBremer,AndreasKretschmer, JaninaLaurila-Dürsch,SebastianNaumannandChristophReiß

IntegrationofStationaryandWearable SupportServicesforanActivelyAssisted LivingofElderlyPeople:Capabilities, Achievements,Limitations,Prospects—A CaseStudy

RainerLutzeandKlemensWaldhör

Abstract Withintherecentthreeyears,astationaryhomeassistancesystemhas beendeveloped,continuouslyoptimizedandoperatedforsupportingseniorsof veryhighage.Inthelastyear,thescopeofthesystemhasbeenextendedby functionandbeyondthespatialbordersofthefamiliarhomebyasmartwatchwith integratedcellularradio(SamsungGear™ S)asawearabledevice.Allcondensed datafromthedifferentstationaryandmobilesensorsaretransferredtoandcollected byacentralserverforlong-termanalysis.Thetechnicalstructureofthesystemis presentedanditscapabilitieswillbedescribed,especiallywithrespecttothe variationofcollecteddataovertimeinthecourseofaprogressingdementiaofone oftheinhabitants.Thedifferentachievementsandperceivedvalue,whichthe systemdeliverstoitsusersandtheirrelativesoverthecourseoftheyearswillbe presented.Butalsothelimitationsofthecurrentlyavailabletechnologyincomparisontheactualdemandoftheinhabitantsandtheirrelativeswillbecharacterized whichdeﬁnestheboundaryconditionsandguidelinesforfurtherresearch.

Keywords Wearables ⋅ Smartwatches ⋅ Stationaryhomeassistancesystems ⋅ Activelyassistedliving(AAL) ⋅ Sensorfusion ⋅ Long-termanalysis ⋅ Elderly people ⋅ MCI ⋅ Dementia

1ProblemDescription

Formostelderlypeoplestayingintheirfamiliarownhomeaslongaspossibleina self-determinedandsafemannerwayisaveryhigh-rankingtarget.Butontheother handtechnicalsupportsystemsforanassistedactiveliving(AAL)havenotgained

R.Lutze(✉)

Dr.-Ing.RainerLutzeConsulting,D90579Langenzenn,Germany e-mail:rainer.lutze@lustcon.eu

K.Waldhör

FOMUniversityofAppliedSciences,D45127Essen/Nuremberg,Germany e-mail:klemens.waldhoer@fom.de

©SpringerInternationalPublishingAG2017

R.WichertandB.Mand(eds.), AmbientAssistedLiving, AdvancedTechnologiesandSocietalChange, DOI10.1007/978-3-319-52322-4_1

abroadacceptanceamongtheelderly.Inourcasestudyworkwedescribe(a)the technicalassistancesolutionimplementedanditsdeliveredbeneﬁtsagainstseveral concernsoftheelderlyand(b)theactualusageandperceivedbeneﬁtsforasenior coupleofhighageinatypicalcare-givingsituationwhereonespousegivescareto theotherinthepresenceofbeginningandprogressing(vascular)dementia.The durationofourstudycoversaserviceperiodofnearlythreeyears,forwhichthe assistancesystemwithitsincreasingfunctionalcoveragehasbeeninuninterrupted 24/7operations.

Wehavechosenthecasestudymethodbecauseakeygoalofourresearchwasto performanin-depthanalysisoftheusageofsuchasysteminareallivescenariofor severalyearsthuscombiningbothaqualitativeandquantitativeapproach.Amain concernaboutapplyingAALtechnologies(cf.[1])dealswithnotreallyinvolving theelderly,butjustfocusingonthetechnicalimplicationsratherthanincludingalso thepsychologicalandsocialaspectsofsuchAALsystems.Therelativelylong studyperiodofnearlythreeyears includingthechancetoinvestigatethe upcomingandintegrationofanewtechnologylikesmartwatches gaveusthe uniqueopportunitytogetnewinsightsintothisdifﬁcultareaofapplyingnew technologicalinnovations.Thusthestudyisbasedbothonqualitativedataderived fromindividualexperiencesandtalkswiththeinvolvedpartiesandquantitative dataderivedfromthetechnologicalsystemsused,alsocombiningbothdatawhen necessary.

Inordertomonitorthewellbeingofpersonsinneedofsupport,care,fortheir dailylivingandhealthweuseanestablishedtechnicalapproach,thedetectionof the activitiesand eventsof daily living(ADLs,EDLs)(seeSect. 3.1 fordetails).1 EDLsmaybe»falls« oreventslike»retiringtobedatnighttime« or»gettingupin themorning«, whichdeterminethebeginning,endinganddurationofthe ADL»bedtime« rsp.»nightlysleep«. TheADLandEDLdetectionisperformed viastationarysensorsdeployedwithinthehomeorsensors,smartwatches,wornby theresidentsontheirwrist.ADL/EDLdetectiontypicallyincludessensorfusion frommultiplesensordevices.AfterpossiblycombiningmatchingEDLsintosingle ADLs,theduration,presenceandintervalsbetweenADLsovertimeisanalyzed. Theseparametersarecombinedinawellbeingfunctionw(t)(seeSect. 3.2 below fordetails),whichcharacterizestheassumedwellbeingresp.healthstateofa monitoredpersonataspecifictimet.Wheneverw(t)fallsbelowapredefined threshold,a healthhazardalert willbeissued.Beforeexternalhelpwillbecalled, thepersoninneedofsupportresp.theirpresentrelativesorcaregiversalways receivealocalpre-alertgivingthemtheopportunitytocanceltheupcoming externalalertandto filteroutoccasionalfalsealerts.Externalhelpcanbeprovided ororganizedbydistantfamilymembersorhomeemergencycallcenters(HECCs). Inordertoclarifythespeci fichazardoussituation,externalalerthandlingalways

1ADLshavebeenacentralissueinorganizingprofessionalnursingpracticeandfordetermining theindependencystatusofelderlypeople,theyhavebeenintroducedbySidneyKatzmorethan 60yearsago.InGermany,LilianeJuchlihaselaboratedtheseADLsforasystematicprofessional caremanagement[2].Inourwork,wefocusonasmallsubsetofcomputationallytractableADLs. 4R.LutzeandK.Waldhör

includesestablishingadirectspeechconnectiontothehomeresp.thepersonin needofsupport.

Theacceptancebarriersofelderlypeopleagainsttheutilizationofsuchassistancesystemshavemultipleaspects:

• Fearofstigmatization.VisibleAALsystemsclearlydemonstratetooutsiders, butalsofortheusersthemselves,thattheuserindeedneedssupportfororganizingthedailyliving,asituationthattypicallyeveryonewanttocamouﬂageas longaspossible.Apotentialsolutionistheuseof “dualuse” deviceslike smartwatches,whichcannotbeeasilyidentiﬁedintheiradditionalassistive usage.

• Resistancetochange,especiallyiftheinstallationoftheassistancesystemat homeiscombinedwithdemolitionwork.Thisgeneralhumanattitudegrows withincreasingageandcanonlybecompensatedbyminimizingthenecessary constructionworkfortheassistancesystem.

• Privacyconcerns.Ofcourse,thecontinuousmonitoringofone’sdailyliving byanassistancesystemproducesaconstantfeelingofdiscomfortandraises concernsaboutthepotentialmisusageoftheaccumulateddata.Especially imagingtechnologies despitetheirtechnologicalachievements arerejected.

• Costs.Highinstallationcosts,esp.forstationarysystems,areanotherimportant keypointfornotusingassistancesystems.Forabroadacceptanceandmass distributionofsuchassistancesystems,anacceptablelevelofassistanceservices hastobeprovidedatreasonablecosts.[4]reportsthattheaveragecostsGermanswouldacceptarearound20 € servicecostspermonth.In[5]thiscost estimationisalsoconﬁrmedfortheUSwithmonthlycostsofUS$25reported fortheCaliforniabased Lively service(withinitialsetupcostsofUS$40).

Theseaspectsallcondenseintheself-insightthatwiththeusageoftheassistance systemthefinalphase inone’slifehasbegun,foreseeablefollowedbythe death. Peopletendtodelaythistypeofintrospectionaslongaspossible.Thusthese technologicalsolutionsaremainlyperceivedthroughthesenegativeconnotations, notbythepositiveaspectsassociated.Ithasbeenargued[6]thattheuserconcerns canbealleviatedandabuyingdecisionforsuchatechnologycanbeboostedby stressingthe multivalentutility oftheassistancesystemnotonlyfor support services,butalsoforimproving comfort and safety athomeaswellasforimproving the energyefﬁciency ofthehome(byreducedheating,coolingcosts).

2SystemStructure

Thesystemdevelopedoverthecourseoftheyearsconsistsofthefollowingthree components(seeFig. 1):

1.A stationaryassistancesubsystem inthehomebasedonhighqualitypresence sensors multisectorPIRsensors ineachroom(cf.[7]foradetailed

Fig.1 Systemstructurewithcomponents:1.Stationaryassistancesubsystem(bottom),2.Smartwatches(top)and3.LTAS(middleleft).Involvedinthecommunicationprocesses,butnotpartof theassistancesystem,isthehomeemergencycallcenterorfamilymembersonduty(middle right),whichreacttoalertsandsmartphones(offamilymembers,relatives)asendpointsofLTAS services(upperleftcorner)

description,Figs. 2, 3).Sensorfusionandthelocalmonitoringisperformedby aSiemensLOGO™ SPS/PLC.ReportingofdetectedEDLs,ADLstotheLTAS server(viahttp)andalerting(viaE-Mail,SMS)isdoneinahighlyavailably

Fig.2 Twochannelpresencesensorsusedforthestationaryassistancesubsystem(Theben Ofﬁce™) 1stchannelfor(local)automaticroomlightingcontrol,2ndchannelforpresence signalingtoassistancesystem

wayviaWANandalsocellularnetworkbyanINSYSIMO-1™ GRPS router/rule-basedfaulttransmitter.Theassistancesubsystemsimultaneously alsoactsasthecenterofalocalhomeautomationsystem(Fig. 4).

2.Smartwatchesas wearablesdevices togetherwithourdevelopedassistanceapp onthewristofthepersoninneedofsupportfortheirdailyliving/health.Here weusetheSamsungGear™ Ssmartwatchwithitslarge2’’ AMOLEDdisplay, GPSandintegratedcellularradio,whichcanoperateindependentlyfroma coupledsmartphoneandisequippedwithourTizen™ assistanceapp(cf.[8]for adetaileddescriptionandFig. 5).Thecellularradiotransmitstherecognized ADLs,EDLstotheLTASserverviahttp.Inadditiontocommunicatingthe wearer’sgeographicpositionbySMSthesmartwatchappalsoestablishes speechconnectionstothewearerofthesmartwatch.Thesmartwatchappwill alsodirectlycommunicatewiththestationaryassistancesubsystem,e.g.to informitaboutthedepartureofthewearerfromhomeandhisreturn.

3.The long-termanalysisserverLTAS,whichcollectstheADLs,EDLstransmittedby1,2andperformsthelong-termstatisticalanalysisofthedata. AsmartwatchappaccessingtheservershowsthelastperformedADLsandvital signsofthepersoninneedofsupportondemand.TheLTASwillproactively informrelativesand/orauthorizedpersonsaboutsubstantialdeviationsofthe usuallifecycleofthepersoninneedofsupport.Ifconﬁgured,theserverwill

Fig.3 CenterofstationaryassistancesubsystemwithSiemensLOGO™ SPS/PLCandinsys IMO-1™ rule-basedfaulttransmitterandGPRSrouter(upperrow),powersupply,industryIP switch(middlerow)andelectricpowersensorandcontactorfortheelectricstove(lowerrow)ina standard3-rowjunctionbox

Fig.5 SmartwatchSamsungGear™ Swithassistanceappdisplayingcommunicationand orientationinformation(holidays,birthdays, .)onthe left andanadvicetoreturntohomeafter leavinganagreedarea(geofencing)onthe right

alsoprovideadaily/weeklysummaryofallhealth-relevantactivitiesofthe monitoredperson(seeFig. 6).

Historically,thestationarysubsystem(1)hasstarteditsproductiveuseinthe earlyspringof2013,thesmartwatchapp(2)wentintodailyuseinspringof2015 andtheLTAS(3)startedits24/7operationonJune1,2015(Fig. 7).

Fig.4 Threelayersmartwatchapparchitecture

Fig.6 ServicesforfamilymembersdeliveredbytheLTAS:ondemandrequestsforthelastvital signsfromthepersoninneedofsupport/care(left),warningofsubstantialdeviationsoftheused circadiancycle(middle),regularreportssummarizingthelastday(right)

Fig.7 Typicalagilityfora24hperiodbasedonan α =0.1(givingheavyweightsforhistorical values).Ascanbeenseentheagilityvalueisfarabovethealertthresholdindicatingthatnoagility problemsarepresent.Leftscaledenotestheaccumulatedsteps(orange actualstepsoftheday, blue theestimatedaccumulatedstepsfortheperiod, grey theagilityvalueß3 and yellow thealert thresholdforß3)

3SystemImplementation

Forthestationaryassistancesubsystem,manysensorsandcommerciallyavailable smarthomesystemshavebeenevaluatedandtested.Forsensors, ﬁnallythe decisioninfavorofhigh-end,twochannel presencesensors wastakenbecause standardmovementsensorstypicallyissuedanunacceptablehighrateof falsenegatives, ifpeopledonotconstantlymoveheavily(cf.[7]fordetails).Fora discussionofpotentialalternativesensortechnologies,seeSect. 5 ofthisdocument. Onechannelofthesensorsisusedfordirectcontroloftheambientlighting;the secondchannelisusedforsensingthepresenceofapersontotheassistance subsystem.

Thelocalusageofthesensorsalsoforroomlightingcontrolwasdeliberately chosentodemonstratethe comfortvalue ofthesystemtotheresidentsonadaily basis.Thisautomaticambientlightingcontrolwasinitiallyinfactjudgedbythe inhabitantsofthehomeasthemostimportantandvaluablefeatureofthenew technologyforthem,althoughtheywereawareofthemuchmoresophisticated assistanceandmonitoringtechnologyinthebackground.Thepresenceofthis subsidiarymonitoringtechnologyforhealthhazardsinthehouseholdwascreating onlyawarenesswhenhealthhazardpre-alert/alertsoccurredfromtimetotime.

Typically,theavailablesensorbasisofcommerciallyavailable(enduser)smart homesystems,(e.g.RWESmartHome ),isnotreliableenoughcomparedtoour presencesensorschosen.Moreimportant,itwasnotpossibletoimplementthe sensorfusionalgorithmsand finitestatemachineswhichareattheheartofthe monitoringprocess(cf.[7, 9])inanecessary fine-grainedandpreciseway. Thereforewe finallydecidedforanimplementationbasedontheSiemensLOGO™ SPS/PLCandInsysIMO™ GPRSrouter/rule-basedfaulttransmitter,which as provenindustrialcomponents workveryreliablealsointhepresenceofcasual powerbrownoutsandblackouts.

TheknowledgeforADL,EDLrecognitionandthelocalhandlingofhealth hazardsonthesmartwatchisempirical,bestpracticeknowledgewhichisgrowing andchangingonadailybasis.Themaintenanceofthesoftwareencodingthis knowl-edgewitheconomiccostsisaseverechallenge,especiallybecauseallhealth hazardsmustbedealtwithsimultaneouslyandthehazardhandlingisintertwinedin itsexecution.

Wehavedevelopedathreelayerarchitectureforsmartwatchappswhichallows toseparatethisknowledgeintoindependent,smallmanageablechunks(cf.[10]and Figs. 4, 8):

• The lowerlayer containstheADLandEDLdetectionbasedonsensorfusion. ForsimpleEDLs,like leavingresp.reenteringanagreedvicinityaroundthe home, thisdetectionwillbedonebytrigonometricmathcalculationsbasedon thecurrentGPSsensordata.ComplexADLdetection,forexampleforthe detectionof ﬂuidingestion,drinking(see[11–13])utilizesneuronalnetworksor

Fig.8 FinitestatemachineforjointhandlingthehealthhazardsresultingfromtheADLs: »absencefromhome«,»runawaysituation«

statisticregressionmethodsforachievingthetask.Thenetsresp.statistic parametershavetobetrainedbeforebyseveralhundredsofsupervisedsamples, inordertoachievethetargetedprecisionandrecallrateofaleast90%(cf.[13] fordetailsofthisprocess,whichincludesdataconditioningandminingofthe sampledataonstandardPCs).Inadditionthissoftwarelayercontainsthe necessaryfunctionalityformanagingspeechconnections,callswiththe smartwatchandprovidesthenecessary orientationinformation presentedtothe smartwatchwearer.Suchorientationinformation seeFig. 5 hasbeenproven tobeofhighsubstantialvalueespeciallyforpersonswithMCIorbeginning dementia,inordertocompensatetheeffectoftheirfailingmemoryincommunicationsituationswithotherpersons.

• The mediumlayer ontopoftheADL/EDLdetectionlayercomprises finitestate machinesforrecognitionandhandlingofhealthhazards.Asinglestatemachine represents,inadeclarativeway,theprocessingofan individualhealthhazard bythesmartwatch(cf.[10]fordetails).The finitestatemachineisdescribedby itsstatetransitiontable,includingthecorrespondingactionstobeexecuted whenenteringastate,andthestatecontentsdescribingtheoutputofthe machineonthesmartwatchscreenwhilebeinginaspeci ficstate.Forexample, Fig. 8 describesthehandlingofanexcessiveabsencefromhomeeitherby (i)leavingaagreedvicinityaroundthehomeand/or(ii)extendingtheoutside staybeyondanagreedmaximumduration.

• The upperlayer containsthecentralschedulerforsynchronizingthesimultaneousoperationsofthe finitestatemachinesfortheindividualhazards,thus allowingthesmartwatchtomonitordifferenthealthhazardssimultaneously(e.g. leavingagreedareas,excessiveabsence,falls,insufficientliquidingestion, abnormalheartrates, …).Theschedulingalgorithmselectsthecontenttobe displayedresp.interactionsequencewiththewearertobeperformedata speci ficpointintime,selectingtheactualstatewithhighestpriorityfromall statemachine(cf.[10]fordetailsoftheschedulingalgorithm).

Withthecurrentgenerationofsmartwatches,thehighpowerconsumptionofthe GPSsensorandthelimitedcomputationalpoweroftheCPUincontinuously condensingallthesensorsignalsandcomparingthemagainstthetrainedpatterns forADL/EDLrecognitionlimitstheusageofthesmartwatchtoatmost18hbefore thewatchneedstoberecharged.Thesmartwatchthereforeisnota24hassistance device,butcanonlybeusedbetweenrisingupinthemorningandretiringtobedat night.Atnighttimethesmartwatchdevicewillbetypicallyrecharged.Assistance duringthistimecanonlybeprovidedviathestationaryassistancesubsystem.

Adirectcommunicationlinkbetweenthesmartwatchesandthestationary assistancesubsystemassuresthatthedeparturefromhomeandalaterarrivalcanbe managedwithoutinvolvingthelong-termanalysisserverLTAS.Thepresenceof thesmartwatchwearerathomeisdetectedviaaccessibilityofthehomeWi-Fiwith aknownSSID,thusallowingtodetectthedeparturefromhomebylossofthe Wi-FisignalandlaterreturnbyreconnectingtothehomeWi-Fi.Thisismuchmore energypreservingthanusingGPS.Thestationaryassistancesystem actingasa

homeautomationsystem canthenswitch-offcriticalelectricloads,e.g.the electricstove,duringtheperiodofabsence.

TheLTASrecordsallEDLs/ADLsreportedfromthestationaryassistance subsystemsandthesmartwatchinarelationaldatabase.MatchingEDLslike departurefromhomeandlaterarrivalwillbefurthercondensedtoacommonADL, e.g.theperiodofabsencefromhome.Inordertorecognizesubstantialdeviations fromdailyroutineresp.thelearnedcircadiancycle,theLTASneedstobetrained ﬁrstforatleastoneweek.Duringthisweekitwillobserveandrecordthenominal valuesforthepresence,thedurationofADLsandtheintervalsbetweendifferent ADLsonaweekdayspeciﬁcbasis.Lateronthenominalvalueswillbeadaptedbya timeseriesanalysiswiththeactualvaluesmeasuredinsubsequentweeks(see Sect. 3.3).Basedonthisinformation,theLTAScanthendeliverthreesubtypesof services(cf.Fig. 6)tofamilymembers,relatives,caregiversoragentsondutyina homeemergencycallcenterduringanincomingcallfromthehomeorfroma smartwatchwearer.

3.1EDLsandADLs

Currently,thesystemrecognizesandanalyzesthefollowingEDLs(eventof daily living):

E1 fall,tumbling

E2 leavinghome

E3 returningtohome

E4 leavingaagreedvicinityaroundthehome

E5 returningintoaagreedvicinityaroundthehome

E6 getting-upinthemorning

E7 retiringtobedatnighttime

E8 fallingasleepforanap

E9 awakingfromanap

E10 lowbatterysituationofthesmartwatch.

AllEDLshaveastheircharacteristicattributethetimet,atwhichtheyhappen, butmayhaveadditionalattributes(e.g.theactualbatterylevelforE10).

AnADL(activityofdailyliving)Ai iseitheratomic(e.g.A5,A6)orstructured andthencharacterizedbytwoEDLse1,e2 happeningattimest1,t2witht1<t2as theirstartandendevents,whichwedenoteasAi[e1,e2]andthusAi hasaduration ta=t2 t1.IfforsuchastructuredAi,itscharacteristicstartevente1 hasbeen recentlyrecognized,buttheendevente2 ispending,Ai willbedenominatedas ongoing. SeeFig. 9 foradepictionofthestructuredADLsA1 toA4

Fig.9 SomeEDLsandADLs,includingtheirspeciﬁcstartingandendingeventswithinthedaily routine

A1 Bedtime,nightlysleep,deﬁnedbyA1[E7,E6]

A2 Absencefromhome,deﬁnedbyA2[E2,E3]

A3 Runawaysituation,deﬁnedbyA3[E4,E5]

A4 (Midday)nap,deﬁnedbyA4[E8,E9]

A5 Visittothetoilet

A6 Fluidingestion,drinking.

AlsoADLshaveattributes,ingeneraltheiractualdurationta,butalsomore speci ficattributes,asthetimespendinthetoiletroom(excludingtheway to and from thetoilet)forA5 ortheamountfor fluidingestedforA6, (thedeterminationof thetypeofingested fluidstillanunsolvedissueinourwork).

3.2WellbeingCalculationandMonitoring

Forthewellbeingfunctionwweproposea(functional)combinationofatleastthe threewellbeingaspectsmeasuringthe inactivity (ß1)ofthepersoninneedof support,theexcessdurationofongoingactivities (ß2)andthe agility oftheperson inneedofsupport(ß3).Fortheseaspectswedeﬁnethreesub-functionsß1,ß2,ß3 mappingSensoricEvents → [0,1],[0,1] ⊂ ℜ.Afunctionvalueof1denotesideal wellbeing.Ahealthsituationisjudgedthemoredangerousthemorethosevalues

decrease.Ifthewfunctionvaluefallsbelowadefinedthreshold,e.g.0.5,an automaticalertwillbeissued.Withrespecttothefollowingdefinitionsthis thresholdvalueof0.5willbereachediftherelated current valueforADLsdeviates by70%fromthespecified nominal value.

WhennorecognizedADLinthehouseholdistakingplace,ß1,thewellbeing sub-functionfor inactivity measurement,isappliedbasedonthedeﬁnitionin[3],by:

wheretisthecurrent(time)durationofinactivitysincecompletionofthelastADL, andTisthespeci ﬁcaverageinactivitybetweenADLslearnedfromthepastforthe currentdayoftheweek.2

Ontheopposite,aslongasarecognizedADLisongoing,ß2,thewellbeing sub-functionforthemeasurementof excessduration ofthisspeciﬁcADLswillbe applied,whichhasbeendeﬁnedin[3]to:

eðTN taÞ TN ,forta>TN 1,otherwise

wheretaistheactualdurationofthe(ongoing)ADLandTNisthespeci fic maximumdurationofthecorrespondingrecognizedADLina normalsituation learnedfromthepastforthecurrentdayoftheweek(Assumingatleastatypical oneweekcycleforcalculatingthespeci fic,possiblyvaryingT,TN,SN,Fnominal valuesfortheindividualdaysofaweek.Ifthecurrentdayunderconsiderationisa nationalorlocalholiday,basedontheWesternculturalcontext,thevaluesforthe lastSundaywillbeusedinstead.).Forsimplicityreasonsofcalculatingß2,itisnot alwaysnecessarytoknowwhichADLexactlyishappening,butthesetofallADLs willbepartitionedintocategorieswithrespecttosimilartypicalexecutiontimes, andthenallADLswithintherespectivepartitionwillhavethesamemaximum executiontimeTN.ß2 willthenbecalculatedwiththespeci ficTNvaluebasedon the category oftherecognizedADL.

IndependentfromdetectedEDLs,ADLsoccurringresp.beingcarried,we proposeforthenewwellbeingsub-functionß3 determiningthe agility oftheelderly subject:

e stpðtÞ STPðtÞ ðÞ STPðtÞ ,forstpðtÞ <STPðtÞ andNOTðE1 Þ 1,forstpðtÞ ≥ STPðtÞ andNOTðE1 Þ 0,ifeventE1 hasbeendetected

2Assumingatleastatypicaloneweekcycleforcalculatingthespeciﬁc,possiblyvaryingT,TN, SN,Fnominalvaluesfortheindividualdaysofaweek.Ifthecurrentdayunderconsiderationisa nationalorlocalholiday,basedontheWesternculturalcontext,thevaluesforthelastSundaywill beusedinstead.

wherestp(t)isthesumofstepsperformedduringthecurrentdayuntilactualtimet, F(t)isthecumulativedistributionfunctionofstepsovertheday,SNisthespeci ﬁc totalnumberofstepslearnedfromthepastforthecurrentdayoftheweekandwith STP(t)=SN*F(t)estimatedfromthenominalstepsumforthecurrentdayattime t.ß3 willbecalculatedallovertheday.Anadvantageofß3 isthatitdoesnotrelyon ADLdetectionandthuscounterbalancestheup-to-datedependencyofthewellbeingcalculationfromtheplenitudeofrecognizedADLs.Thesubfunctionß3 will dependprimarilyonthe(counted)stepsmeasuredbythesmartwatchoverthe courseofthedayandtakingintoaccountthecumulativedistributionofthosesteps untiltimetofthespeci ﬁcdaylearnedfromthepast.Arecognizedfallwill immediatelyeffectanalert(seebelow).

Finally,thewellbeingfunctionw:SensoricEvents → [0,1],[0,1] ⊂ ℜ,willbe formallydeﬁnedas:

w=minß1 ,ß2 ,ß3 fg

Thismeansthatwheneverthe inactivity (missinganyrecognizedADL) orthe excessduration ofanongoingactivitycategory orthelacking agility getscritical andthewvaluefallsbelow0.5,a healthhazardalert willbeissued.

3.3LearningtheNominalValues

FortheacquisitionofthenominalvaluesforT,theTNsforeachADLcategory,SN andthecumulativemovementdistributionfunctionF(t)aseasonalcycleofone weekandaprecedinginitialtrainingphaseofaweekwillbeused.Foreachday s ∈ Nat,s=1,2… 7inthetrainingphaseandthefollowingweeks,the(weekday) speci ﬁcTs,theTNs foreachADLcategory,SNs andFs(t)valuesresp.distributions areestimated:

• Inactivity:ForallADLsA1 … AX detectedonthisdays,forTs theaverage (I1 +I2 … +Ix 1)/X 1ofallinactivityperiodsI1 … Ix 1 willbeused,where In denotestheinactivityperiodbetweenAn andAn+1.

• Excessduration:Forallactivity(time)durationsd1, … dX ofthoseADL instancesA1, AX detectedonthisdaysforaspeci ﬁcADLcategory,forTNs forthisADLcategorythemaximumdurationMAX i=1, ,x (di)willbeused.

• Agility:Foreachhourh=0 23oftheday,thesumofallcountedsteps achievedbytheendofahour(fromthebeginningoftheday)willbetabulated forcomputingtheinitialFs(h)distribution.SNs willbethetotalnumberofall stepscountedforthisdays.

Forprojectingthesevaluesforthenexttimefordays,s>7,weusethe exponentialmovingaveragealreadyproposedin[14]forMA=T,TN,SN,F:

whereOx istheobservedvaluefordayx(computedbythemethoddescribed above)andMAx isthecomputedmovingaveragefordayxand α,0 ≤ α ≤ 1, α ∈ ℜ,isa “smoothingconstant”,whichmaygivemorerelativeweighteitherto observedvaluesintheweekbeforeorthepredictedvalue,movingaverage,forthe speci ﬁcdayintheweekbefore.InitiallyMAi =Oi foralli=1, ,7.TheseasonalityfactorTRx coversapotential datatrend byaccountingthedifferencesinOx valuesforsubsequentdaysofthelastweek(cf.[3])andwillbecontributingstarting fromthethirdweek(s=15),forallpriordaysi ≤ 14:TRi =0.TRx isdeﬁnedby:

Figure 7 showsatypicalexampleofanelderlyfortheagilityparameter:

4Experiences

4.1DevelopmentofFunctionalityOvertheYears

Duringtheinitialyearsofoperation,whenthestationaryassistancesubsystemwas theonlyavailablecomponent,themainuseoftheassistancesubsystemwas:(i)the monitoringofinactivityperiods viaß1 atdaytime(“inactivityanalysis” , cf.[6, 15]) and(ii)theß2 monitoringofan excessivedurationofthe ADLs »bedtime«and» visittothetoilet«.Atthattime,weusedanadditionalADL »motionathome« in ordertohaveamorestructureddaytime.Although,inamulti-personhousehold thepersoninneedofsupportandhisspouse,guestsinthehome thisADLdidnot reallyallowedtoconcludespecificallyaboutthehealthstateofthepersoninneed ofsupport.Theintroductionofthesmartwatchthereforebroughtasubstantial progress,becausethemovementinformationfromnowoncouldbeassigned directlytothepersoninneedofsupportandsupervisedbyß3;thustherecognition ofthisinitialADL »motionathome« wasabandonedwiththeavailabilityofthe newsmartwatches.FortherecognitionofADLsA1 »bedtime«,A5 »visittothe toilet«,thestationaryassistancesubsystemachievedadurableprecisionrateof about98%basedonthefalsepositiveandnegativealerteliminationtechniques describedin[7].Inessence,theutilized finitestatemachinedidsoinheuristically applyingthefactthatpersonscannotarbitrarilyappearordisappearinthespeci fic roomsofthehomeotherthanenabledby(i)theconnectivityoftheroomsand (ii)basedontheirroomspeci ficlocationinsidethehome(oroutside)uptonow.

Withthe ﬁnitestatemachinesinthebackground,especiallythesequencingof activitiesforanightlyvisitofthetoilet,ADLA5,startingandendingfromandin

thebedroomwassupervised.Thetransittimesfromthebedroomtothetoiletand backagainweresocharacteristicfortheindividualpersonsthatthesetoiletvisits couldbeclearlyassignedtotheindividualpersons.Weusedthisinformationto automatically ﬁll-upthedailynursingrecordrequestedbythehealthinsurance.We furtherobservedinthecourseoftheprogressingdementiaillnessofthepersonin needofsupportthatthetransittimenecessarytopassadistanceofabout10mgrew withintheoperationtimeoftheassistancesystemnearlybyfactoroftenduetothe proliferatingdisorientationofthepatient.Pre-alertsoftheassistancesystematthat timeprimarilyaddressedresp.targetedtogiveattentiontoresp.towake-upthe care-givingspouseorfamilymemberpresentinthehomeinordertoverifythat everythingwasokwiththepersoninneedofsupport.Withtheprogressing dementiaandthenoticeableexhaustionofthecare-givingpersonsbythemoreand moredemandingcare-givingtask,thenightlyß2 inducedpre-alertsgainedmoreand morepracticalimportanceoverthecourseoftheyears.Theexhaustionofthe caregiverswasnotonlycausedbynumberofnightlytoiletvisitscontinuously increasingduringtheprogressofthedementiaillnessandaccompaniedbyan increasingfrequencyofß2 excessdurationalertsforthosevisits.Alsothe increasingfrequencyofnightlyunrestperiodsinthebedroom,wherethepersonin needofsupportwasnotabletosleepcontinuouslymorethan3h,contributedto this.Similarscenarioshavebeenreportedtousbymanycare-givingrelativeswith respecttotheirfamilymembersinneedofsupportandimpairedbydementia.At thisstageofthedementiaillness,twoyearsaftertheintroductionoftheassistance technology,therealvalueoftheinstalledtechnologyinprovidingmuchmore supportthanautomaticambientlightingwasfullyunderstoodandperceivedbythe care-givingspouseandfamilyrelatives.

Ifalocalpre-alertremainsunanswered,thestationaryassistancesubsystem placesanautomaticalertviaSMSandE-Mail.Thefollowingexternalhandlingof suchalertssuffersfromthesamelimitationsasforthetraditionalhomeemergency calldevices:ifthecheck-backtelephonecalltothehomealsowillnotbeanswered, complicatedresearchhastobeinitiatedbythealertservingagent.Thisincludesa potentialquestionnaireoflocalneighborsbytelephone …,inordertodecide whetheracostlyon-siteemergencyinterventionshallbedonersp.anintervention teamshallbesendouttothealertinghome.

Now,withtheavailabilityoftheLTAS,thealertservingpartycanimmediately retrievecontextualinformationabouttherecenthistoryofADLsinthecorrespondinghome,evenifthecheck-backcallwillnotbeanswered.Thisallowsfaster andmorefactbaseddecisionsaboutsendingouttheemergencyinterventionteam, especiallyatnighttime,whennoneighborscanbeasked.Moreover,withthecellulartelephoneincludedinthesmartwatchwornatthewristofthepersoninneedof support,thelikelihoodofestablishingasuccessfulspeechconnectionbetweenthe alertservingagentandthepersoninneedofsupportissubstantiallyincreased.Ifa healthhazardhasbeendetectedbythesmartwatchandalocalpre-alerthasnotbeen answeredbythewearerofthewatch(terminatingfalsealerts)thesmartwatchapp initiatesthetelephonecall.Forsuchanautomaticallyestablisheddirectspeech connectionfromthepersoninneedofsupport,thepresenceofaseverehealth

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“forte” or “piano,” one beat in advance be adhered to.

CHAPTER III-B

P W I F F

P U A C

No. 1. Preparatory position. 4/4 time.

N 2

Position of the first beat in 4/4 time. N 3

Position of the second beat in 4/4 time.

N 4

Position of the third beat in 4/4 time. N 5

Position of the fourth beat in 4/4 time.

CHAPTER III-C

D G U C

The music examples are to illustrate the use of the gesture and have been found practical for class work.

In practising these gestures with the music examples, the movement must always be expressive of the character of the music.

Sharp and energetic movements for music of an accentuated character, and moderate, gentle movements for music of a corresponding nature.

The accent is executed by a sharp, quick arm movement. Great care must be taken to execute each movement, even the most gentle pianissimo, clearly and with authority.

In all the diagrams shown the following principles are adhered to:

1. The heavy or accented beat is indicated by a dark arrow.

2. The light or unaccented beat is indicated by an unshaded arrow.

3. The semi-accented beat is indicated by a semi-shaded arrow.

4. All subdivisions are indicated by dotted lines.

5. The fundamental beats are described with the arm movement, while subdivisions are performed with the wrist. In this manner, a

clear indication of the fundamental beat is always maintained.

DIAGRAM Nᵒ. 1

Fundamental method of beating 2/2, 2/4 time.

DIAGRAM Nᵒ. 2

Actual method of beating 2/2, 2/4, and fast 6/8 and 6/4 time.

EXAMPLE Nᵒ. 1 for DIAGRAM Nᵒ. 2

[Listen]

Accented 1st beat:

Accented 2nd beat:

[Listen]

DIAGRAM Nᵒ. 3

Normal subdivision of 2/2 and 2/4 time.

N.B.The subdivision of each beat is indicated by the word “and”.

EXAMPLE Nᵒ. 2 for DIAGRAM Nᵒ. 3

[Listen]

[Listen]

This form of six eight time is indicated in the above manner.

DIAGRAM Nᵒ. 4

Method of beating 6/8, 6/4 time when only 2 beats in a measure are required. To be used also for slow 2/4 time.

DIAGRAM Nᵒ. 5

Accented subdivision of 2/2 and 2/4 time.

EXAMPLE Nᵒ. 3 for DIAGRAM Nᵒ. 5

[Listen]

DIAGRAM Nᵒ. 6

6/4 or 6/8 time. (Modern French Method) 6/4 or 6/8 time is a subdivision of 2/2 or 2/4 time.

DIAGRAM Nᵒ. 6a

Old method of beating slow 6/8 time.

The disadvantage of this method is that the 6th beat is out of proportion with the others. In diagram Nᵒ. 6 the long beat comes on the 4th or naturally accented beat of the measure, whereas in 6a the 6th or last beat in the measure is apt to be unduly accented.