Archetype-Based Modeling of Persona for Comprehensive Personality Computing from Personal ...

25 Pages·2017·7.69 MB·English

sensors
Article
Archetype-Based Modeling of Persona for
Comprehensive Personality Computing from
Personal Big Data
AoGuo1,* ID andJianhuaMa2
1 GraduateSchoolofComputerandInformationSciences,HoseiUniversity,Tokyo184-8584,Japan
2 FacultyofComputerandInformationSciences,HoseiUniversity,Tokyo184-8584,Japan;[email protected]
* Correspondence:[email protected]@gmail.com;Tel.:[phone]
Received:14December2017;Accepted:23February2018;Published:25February2018
Abstract: Amodeldescribingthewidevarietyofhumanbehaviourscalledpersonality,isbecoming
increasingly popular among researchers due to the widespread availability of personal big data
generated from the use of prevalent digital devices, e.g., smartphones and wearables. Such an
approach can be used to model an individual and even digitally clone a person, e.g., a Cyber-I
(cyberindividual). Thisworkisaimedatestablishingauniqueandcomprehensivedescriptionfor
anindividualtomeshwithvariouspersonalizedservicesandapplications. Anextensiveresearch
literatureonorrelatedtopsychologicalmodellingexists,i.e.,intoautomaticpersonalitycomputing.
However,theintegrityandaccuracyoftheresultsfromcurrentautomaticpersonalitycomputing
isinsufficientfortheelaboratemodelinginCyber-Iduetoaninsufficientnumberofdatasources.
Toreachacomprehensivepsychologicaldescriptionofaperson,itiscriticaltobringinheterogeneous
datasourcesthatcouldprovideplentyofpersonaldata,i.e.,thephysiologicaldata,andtheInternet
data.Inaddition,insteadofcalculatingpersonalitytraitsfrompersonaldatadirectly,anapproachtoa
personalitymodelderivedfromthetheoriesofCarlGustavJungisusedtomeasureahumansubject’s
persona. Therefore,thisresearchisfocusedondesigninganarchetype-basedmodelingofpersona
coveringanindividual’sfacetsindifferentsituationstoapproachacomprehensivepersonalitymodel.
Usingpersonalbigdatatomeasureaspecificpersonainacertainscenario,ourresearchisdesigned
toensuretheaccuracyandintegrityofthegeneratedpersonalitymodel.
Keywords: archetype;scenario;persona;personality;personalbigdata;psychologicalmodel
1. Introduction
Withtherapidadvancesinthedigitalexplosionofdataandconnectivity,serviceandintelligence
aregrowinginimportanceinthecyber-physicalintegratedhyperworld[1].Theamountofdatacreated
bythisadvanceissufficienttomakeamodelofanintegratedhumanindividualfeasible,enabling
servicesmorefinelytailoredtoindividualusers. Asoneexample,Cyber-I(cyberindividual),adigital
cloneforarealindividual(Real-I)featuredwithauniqueandcomprehensivepersonaldescription,
has become an ideal framework for pervasive intelligence to better encompass the corresponding
Real-I [2]. However, due to the dazzling yet unstructured abundance of information available,
approachingCyber-Ibysimplyminingpersonaldataischallenging,asaconsequenceofthelackof
maturestructureandhighlevelofdifficultyincomputingaperson’smentalstate. Althoughagrowth
mechanismforCyber-Imodelinghasbeenbuiltinthelaboratory[3,4],i.e.,aCyber-Imodelcontaining
threegrowthmechanisms,namelyBigger,HigherandCloser,withaspeciallydesignedmanagement
system[5],Cyber-Imodelingisstillatthetheoreticalstagewithoutanyactualresult. Becauseofthe
difficultyinascertaininganindividual’smentalstate,thedevelopmentofCyber-Imodelingisstill
at a vestigial stage. As using measuring aspects of personality to describe an individual’s mental
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make-upismainstreaminthefieldofPsychology,amaturetheoreticalsystemtodosoexistswithin
thatdiscipline. Thus,thecomputingofpersonalitybasedoninsightsachievedwithinthatfieldhas
becomeapreferentialaimforbetterCyber-Imodeling.
Personalityisapsychologicalconstructaimedatdescribingthewiderangeofhumans’habitual
behaviors,cognitionsandemotionalpatternsthatevolvefrombiologicalandenvironmentfactors[6].
Currently, a lot of research is focused on or related to personality computing. In particular,
theBig-Five(BF)personalitytraittheory,whichcontainsafive-factormodel(FFM),hasbeenadopted
asatheoreticalfoundationinleadingresearchintopersonalitycomputing,duetoitslongstanding
andwidelyvalidatedresearchfindings. AccordingtothesurveyofVinciarelli,personalitycomputing
canbeclassifiedintothreeareasbasedonthreedifferentapproaches,namelyAutomaticPersonality
Recognition(APR),AutomaticPersonalityPerception(APP)andAutomaticPersonalitySynthesis
(APS)[7].APRaimsatinferringtraitsthroughself-assessment,i.e.,throughaquestionnaire.Incontrast,
APPfocusesparticularlyoninferringthepersonalityobserversattributetoagivenindividualfrom
proximalcues,i.e.,throughthejudgmentbasedonothers’perception. Inaddition,APSisinrelationto
thetaskofautomaticallygeneratingdistalcuesaimedatelicitingtheattributionofdesiredpersonality
traits. ThisresearchhasappliedtheAutomaticPersonalityRecognitionintopersonalitycomputing
duetoitsemphasisoninferringemotionalandsocialphenomenafrommachinedetectablebehavioural
evidence,which,inthiscase,referstotheemotionaldatagatheredfromsurroundingdevices. Thus,
thepersonaldatacollectioncarriedoutinourpreviousworkbyacontext-awareschedulingmechanism
is highly suitable as a resource for Automatic Personality Recognition [8]. However, according
to the survey mentioned above, current personality computing still relies on finding a correlation
betweenaspecificpersonaldatumandthecorrespondingpersonalitytraitdescribedinthefive-factor
model (FFM). These personality traits indicate broad features in terms of behavior, cognition and
emotion. Forexample,peoplehighinopennessaremotivatedtoseeknewexperiencesandtoengage
inself-examination. Structurally,theyhaveafluidstyleofconsciousnessthatallowsthemtomake
novelassociationsbetweenremotelyconnectedideas. Itisevidentthatthisrevelationofapersonwith
highopennesstraitistooinaccuratetodescribeaspecifichumanindifferentsituations. Forexample,
a person may show a poor ability of seeking new experiences when he feels nervous. Therefore,
thecomprehensivemodelingofpersonalityisurgentlyneededtoachieveamoreprecisedescription
ofahuman.
AccordingtothetheoriesofCarlGustavJung(1875-1961),theconceptofpersona,asakindof
maskorsocialrole,representsacompromiseapersonmakeswiththesocietyconcerninghowhe/she
shouldappeartobe[9]withanaimtoconcealthetruenatureoftheindividual[10]. Aperson’smental
structureinvolvesthepersonaand,asshowedinFigure1,themodelthatindicatesthe“self”canbe
describedasconsistingofthreelayers. Astheouterlayerofthisstructure,personareferstothesetofa
human’smentalstateindifferentsituationsorscenarios. Thepersonaisalsotheconnectionbetween
theexternalphysicalworldandtheinnermentalworld. Inordertodelveintoaperson’sinnerworld,
themodelingofpersonaisthereforeneeded. Furthermore,thepersonamakesitpossibletoconstruct
a digital personality model. In view of this, this research is aimed at achieving a comprehensive
personalitymodelfromthemodelingofpersona.
Asweknow,diversepersonaeexistinanyhuman’ssocialinteraction. Themodelingofsufficient
personaeforcommonsituationsisrequiredtoaccuratelydescribeaperson. Theheterogeneousdata
sourcesareselectedaccordingly,toprovidetheplentyofpersonaldata(alsonamedPersonalBigData).
Thispersonalbigdatacoversallthepersonaldatathatcouldbegatheredfromdailylifeforhuman
modeling,i.e.,physiologicaldata,thedatafromtheInternetorthedailyactivitydata. Suchplentiful
dataprovidethesolidfoundationforfuturepersonamodeling. Inaddition,thephysiologicaldatalike
emotionstatesthattimelyreflectingaperson’sresponsesofactivitieswouldhavehighpriorityforthe
constructionofpersona,duetothewarningfromJungians’perspectivethattheresearchersofpersona
shouldpayattentiontohiddenelementsthatmayeruptifnotmadeconscious[11].
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FigFiugruere1 .1J. uJunngg’’ss tthheeoorryy ooff tthhee ““SSelefl”f.” .
Although the personal data is more than sufficient, it seems hard to construct a person’s persona
Althoughthepersonaldataismorethansufficient,itseemshardtoconstructaperson’spersona
without any persona forms. Fortunately, a prototype persona could be derived from the twelve
withotuytpaicnayl hpuemrsaonn atyfpoersm, osr. Farocrhteutynpaetse, lays, aouptrlionteodty ipne Jupnegrsiaonn apscyocuhlodlobgeicdale rthiveeodryf.r Tohme tchoenctwepetl vofe atny pical
humaanrcthyeptyeps,e owraasr cchoentcyepievse,da sbyo uCtalirnl eJudnign [J1u2n] ginia hnisp swyocrhko olong tihcea lctohlleeoctriyv.eT uhnecocnosnccioeupst oasf aan tyapricchael type
was ccohnarcaecitveer dtob ywChoamrl Jaunn ogb[s1e2rv]eirn mhiigshwt oemrkotoionnathlley croeslloencattiev.e Cuonllceoctnivsce iouunscoansscaiotuysp eicmablrcahceadra cter
to whimomperasnonoabl,s eurnviveerrsmalilgyh stheamredo,t iofunnadlalymreenstaoln cahtea.racCteorlilsetcictsiv oef uhnucmoannsictiyo uthsate mheb rraecfeerdreidm tpo earss onal,
primordial images or archetypes [13]. Based on myths, legends and esoteric teachings, archetypes
universallyshared,fundamentalcharacteristicsofhumanitythathereferredtoasprimordialimages
form part of the individual’s unconscious mind [14]. That is to say, a specific persona in a certain
or archetypes [13]. Based on myths, legends and esoteric teachings, archetypes form part of the
scenario would take on the aspect of one or more archetypes. Such archetypes could be detected from
individual’sunconsciousmind[14]. Thatistosay,aspecificpersonainacertainscenariowouldtake
different scenarios. For example, when playing card games, a person may exist as the archetype Hero,
ontheaspectofoneormorearchetypes. Sucharchetypescouldbedetectedfromdifferentscenarios.
referring to his desire to win the game. However, the person could also show the archetype Lover, as
Forexample,whenplayingcardgames,apersonmayexistasthearchetypeHero,referringtohis
he/she may want to help a partner in certain games. Hence, the first target of this research is to create
desirea tpoerwsoinnat dheerigvaemd ien. oHneo wspeevciefirc, tdhiveeprseer sscoennacroiou ledxiastlisnog sahs oaw cotmhbeinaarctihoent oyfp deifLfeorvenetr ,aarcshhetey/psehse. may
wanttoheInlp oradpera trot naceqruinirec ae rctoaminprgeahmenessiv.eH deensccrei,ptthioenfi orf sat htuarmgaent, othfet hseiscornedse oabrjcehctiivset oof cthreisa rteesaeaprcehr sona
deriviesd toin moondeesl pae sceirfiicesd oivf epresresosncaeen areriporeesxeinsttiinngg aa scoamcpormehbeinnsaitvieo ndeosfcrdipiftfieorne notf aar ccehretatyinp epse.rson, or
Ipneorsrodnearlittoy.a Ocqnue iorfe tahec ogmrouppreinhgesn osifv peerdseosncarei pist icoanlleodf afacheut mperasnon,ath (eFPs)e,c ionn wdhoicbhje tchtei vteeromf ‘tfhaicsetr’e isne arch
the psychological field refers to a specific and unique aspect of a person’s mental state or boarder
is to model a series of personae representing a comprehensive description of a certain person,
personality trait, such as the mental state demonstrated on the Internet [15]. Specifically, each person
or personality. One of the groupings of personae is called facet persona (FP), in which the term
could be modeled to one special description called a primary persona (PP), denoting the primary
‘facet’inthepsychologicalfieldreferstoaspecificanduniqueaspectofaperson’smentalstateor
personality state in which a person presents themselves.
boarder personality trait, such as the mental state demonstrated on the Internet [15]. Specifically,
The remainder of this paper is organized as follows: the following section introduces related
eachpersoncouldbemodeledtoonespecialdescriptioncalledaprimarypersona(PP),denotingthe
studies and their relevance to this research. Section 3 provides an overview of each function for
primapreyrspoenrasloitny amliotydesltiantge. Tinhew pheircshonaapl derastao ncopllreecstieonnt sfrtohme mhesteelrvoegse.neous data sources is explained in
TShecetiroenm 4.a Sincednearrioof dtehtiesctpioanp ferromis eovregnatn loizceatdioans, cfoonlltoacwt sp:ertshoen ftool luosweri bneghasveciotrio isn giinvterno dinu Sceecstiorenl ated
studie5.s Saenctdionth 6e diresrcerliebvesa nthcee mtoeththodis forer stehae rcchha.raScteecrtiizoantio3n pofr othvei dtweselvaen aorcvheertvyipeews. Soufbesaeqcuhefnutlnyc, ttihoen for
persoanrachlietytympeo-bdaesleidn gm.oTdheelinpge rosfo pnearlsodnaat aisc goilvleenc tiino nSefcrtoiomn h7.e Atesr othgee pnreoopuossedda ptaerssoounraclietsy imsoedxepll, atihnee din
computing of facet persona and personality are explained in Section 8. Experiments and discussion
Section4. Scenariodetectionfromeventlocation,contactpersontouserbehaviorisgiveninSection5.
are presented in Section 9. Conclusions and future work are outlined in the last section.
Section 6 describes the method for the characterization of the twelve archetypes. Subsequently,
thearchetype-basedmodelingofpersonaisgiveninSection7. Astheproposedpersonalitymodel,
2. Related Works
thecomputingoffacetpersonaandpersonalityareexplainedinSection8. Experimentsanddiscussion
Over a brief period of time, computing has come to focus on personal life through the increasing
arepresentedinSection9. Conclusionsandfutureworkareoutlinedinthelastsection.
prevalence of smart devices. Subsequently, a great deal of research is being conducted into collecting
personal data. Chittaranjan et al., investigated behavioral characteristics derived from rich
2. RelatedWorks
automatically extracted smartphone data [16]. Daniel et al., developed a wearable sensing platform
Otov aeurtaombraiteicfaplleyr icoadptoufret iimndei,vciodmuapl uantidn gcohllaesctcivoem peatttoerfnosc uosf boenhpaveirosro, nwahlilcihfe ist harboleu gtoh bteh eusinedc rteoa sing
prevaplerendciect foufrtshmera ursterd beevhiacveiso.r [1S7u].b Inse aqdudeitniotlny,, coantgerxet-aatwdareeanlesosf tercehsneiaqrucehs hiasveb beeincogmceo anpdpuliccatebdle into
collecttoi npgerpsoenrasol ndaatlad caotlale.ctCiohni. tOtanrea nsajalinenett eaxla.m, ipnlve eosft itghaist eisd obuerh parvevioioruals crhesaeraarcchte rinistoti ccsondteexritv deadtaf rom

rich automatically extracted smartphone data [16]. Daniel et al., developed a wearable sensing
platformtoautomaticallycaptureindividualandcollectivepatternsofbehavior,whichisabletobe
usedtopredictfurtheruserbehavior[17]. Inaddition,context-awarenesstechniqueshavebecome
Sensors2018,18,684 4of25
applicable to personal data collection. One salient example of this is our previous research into
context data detection and data collection scheduling in a smartphone-based client-server system.
Asystematicwearabledatacollectionschedulewasimplementedwithacorrespondingcontext-aware
engine to handle different contextual information. Accordingly, in this research a scenario and a
personaaredetectedseparately, basedonpreviouslycollectedwearabledata, includingGPSdata,
accelerationdata,andsleepdata,inordertocollectinformationintermsofuserbehavior,location,
andevencommunication.
WiththeincreasingusageoftheInternet,thereisapressingurgencytodepictinguserbehaviorin
socialnetworking,whichhasledtothecreationofusermodelsderivedfromthoseusers. UserPersona
asoneusermodelingtechniquetobeusedinproductdesignandHumanComputerInteraction(HCI),
wasfirstputforwardbyCooperin1999[18]. Differentfromthedescriptionofpersonainpsychology,
“personaeusedfortheproductdesignrefertofictionaldetailedarchetypicalcharactersthatrepresent
distinct groups of behaviors, goals and motivations observed and identified during the research
phase”, as defined by Goodwin [19]. To create a fictional typical user in possession of rich detail
forspecificrequirements,Wu,etal.,proposedamethodthatcouldswiftlytransformacustomer’s
needsintoapersonabasedonanalysisoftypicalproductutility[20]. Toaccuratelycreateapersona,
Fakinlede, et al. designed a well-defined robust psychometric model for defining and effecting a
personaanddepictingitsfunctionalrole[21]. AlMaliki, NcubeandAlitriedtobuildpersonaeby
adoptingfeedbackfromgeneratedpersonaeinsteadoffromrealuserstoimprovesoftware. Analysis
orestimationmaybecomeanotherissueforresearchintopersona[22]. What’smore,takingontology
asaconceptualvocabulary,Salmaetal.,modelledandstoredpersonaewithaResourceDescription
Framework(RDF),adescriptivelanguage[23]. Similartotheresearchaboveintopersonamodeling,
inthisresearchtheprevalentpersonamodelisalsoapplied,especiallyinitselements. Inaddition
totheresearchfocusingonpersonamodeling,Phuong,etal.,estimatedthemotivationandlearning
strategyofstudentsfortheimprovementofaneducationalcourse[24]. Additionally,Friessexplored
incorporating personae into the heuristic evaluation of websites [25]. Tu et al. have used cluster
analysis to examine the persona development [26]. Specifically, the Persona Creation and Usage
Toolkitisadoptedtoclusterninecategoriesintermsoftheuser’sbiographicbackground,usergoals,
knowledge, emotional characteristics of usage and etc. Similarly, this research selects two of the
Big Five personality traits, the Emotion Stability and the Extroversion/Introversion to represent the
calculatedpersonality. Takingadvantageofpersonainonlinehealthcommunities,Huhetal,annotate
fourpersonaefromtheclusteringoftheusagebehaviorpatternofonlinecommunitiesbasedonalarge
amountofsurvey[27]. Nevertheless,thesefourpersonaeinJina’sresearch,namelytheCaretakers,
Opportunists,ScientistsandAdventurers,areonlysuitableforclusteringtheusersbelongingtoa
certaingroupwithaspecificpurpose. Incontrast, thisresearchaimsataseriesofstablepersonae
fromdifferentscenarios. Toensurethegeneralapplicabilityforuniversalsituations,eachpersonais
comprisedofoneormoreofJungian’sarchetypes. Inconclusion,therelatedmodelingofpersona
byCooperjustregardsthepersonaasafictionaluseruponitsapplication. Theresultingpersonais
analogoustotherelationshipbetweenabriefresumeandanactualperson. Incontrasttothepersona
researchdescribedabove,onefeatureofpersonamodelinginthisresearchisthecreationofapersona
basedontwelvehumanarchetypes. ThedigitalizationofapersonabasedonJungianpsychological
theorymakesitpossibletoconstructapersonathatdescribesapersonaccuratelyandcomprehensively.
Anotherfeaturedifferentiatingthisresearchfrompreviousresearchistheapplicationofthemodeling
throughfacetpersonae. Thisresearchmainlyaimstominepersonaltraitsforcomprehensivepersonae
sufficientforAutomaticPersonalityRecognition.
Jungian’stwelvearchetypeshavebeenusedbymanyotherstudiesastheyinvolvetheuniversal
goals,motivationsanddesiresinanindividual’sdailylife. Bechteretal. haveidentifiedthecorrelation
between Jungian’s archetypes and personality traits based on the statistics of 102 executive MBA
students[28],suggestingtheusabilityofthearchetypes. Similarly,onthebasisofthesefundamental
archetypes,ourresearchbuildsthepersonamodelingafterconductingatimelyquestionnairewiththe
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testers’. Inaddition,Munteanuetal. havefoundthecorrelationbetweenarchetypesandMyers-Briggs
Personality features [29]. Besides, Pera, Viglia and Furlan have calculated the correlation among
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severalvariablecharactersincludingthearchetypes,phasesandcovariates[30]. Researchinrelation
toJungian’sarchetypeshasindicatedtheapplicationpossibilityofarchetypesforhumanmodeling.
calculated the correlation among several variable characters including the archetypes, phases and
Ocnoveairsisauteesf [o3r0]C. yRbeseera-Ircmh oind erelilnatgioins ttho eJuancgcuiarna’tse aarcnhdetcyopmesp hreahs einndsiicvaetecdo mthpe uatpaptliiocnatoiofna pnoisnsdibiviliitdyu al’s
personofa alirtcyh.etGypenese froarl lhyu,mthaen hmuomdealningm odel can be classified into four layers, as shown in Figure 2.
The bottomOnlea yiessruoef fhour mCaynbemr-Io dmeoldinelvinoglv iess tvhaer iaocucsurhautem aannd stcaotmesp,reshuecnhsiavse thcoempphuytasitioolno goicf aalns tate,
i.e.,heinadrtirvaidteuaaln’sd pberrsaoinnaalictyti.v Giteyn,eorarltlyh,e thbee hhuamviaonr mstoadtee,l ic.aen., brue nclnasinsigfi,ewd ianltkoi nfogu,ro lraytaerlks,i nasg s,heotcw.nF ionr the
Figure 2. The bottom layer of human model involves various human states, such as the physiological
second layer, the person’s pattern such as their activity pattern or emotion pattern are described.
state, i.e., heartrate and brain activity, or the behavior state, i.e., running, walking, or talking, etc. For
More abstractly, the third layer shows the person’s traits in different aspects, for example taking
the second layer, the person’s pattern such as their activity pattern or emotion pattern are described.
part in a hobby such as watching movies, or their working style. These three layers covers the
More abstractly, the third layer shows the person’s traits in different aspects, for example taking part
aspects of a human from the concrete to the abstract. However, the top layer is the modeling of a
in a hobby such as watching movies, or their working style. These three layers covers the aspects of
humaan ’hsupmsaync hfroolmog tihcae lcsotnactree,tne atom tehley apbesrtsraocnt.a Hlitoyw. eTvoetrh, tehpe rteospe nlatydera yis, tthhee rmeohdaeslibnege onf aa ghruematadn’esa lof
researpcshycinhotolopgiecrasl osntaateli,t nyacmoemlyp puetrisnognabliatsye. dToo tnhet hperewseindte dlyayu, stheedre“ hBaigs bFeievne ”a gpreerasto dneaalli otyf rtersaeiatrtchhe ory
depictiinntgo upseersros’nfaeliattyu croemsfpruotmingfi vbeasdeidff oenre tnhtea wspideecltys. uGseudn t“uBkigu Feitvael”, pcreerasotenaaliptye rtsroaint atlhietyormy oddepelicftrionmg the
perspeucsteirvse’ foefautusreers ffarvoomr itfeivse[ 3d1if]f.eVreinntc iaasrpeellcitss.u Grvuenytuedkup eetr saoln, aclrietaytec oam ppeurstoinnagliitny tmeromdselo ffromme atshuer ing,
perceppteirosnpeacntidvee ovfa uluseart fiaovno[r3it2e,s3 [33]1.]A. Vcicnocriadrienllgi stuortvheiysedsu prevresoy,nMaliotyh caommmpuatdiniga nind teVrminsc ioafr melelaisautrteinmg,p ted
perception and evaluation [32,33]. According to this survey, Mohammadi and Vinciarelli attempted
AutomaticPersonalityRecognitionbasedonusers’prosodicfeatures[34]. Inthisresearch,theresearch
Automatic Personality Recognition based on users’ prosodic features [34]. In this research, the
methodsofpersonalityrecognitionhavealsobeenadoptedasoneaspectofpersonalitycomputing.
research methods of personality recognition have also been adopted as one aspect of personality
Thedifferenceliesinperceptionbeingbasedonpersonaeforthegeneraldepictionofanindividual’s
computing. The difference lies in perception being based on personae for the general depiction of an
patterninallaspectsofdailylife,insteadofmerelybeingderivedfromasingleorlimitednumberof
individual’s pattern in all aspects of daily life, instead of merely being derived from a single or limited
certainnusmitubeart ioofn cse.rtain situations.

FigFuigreur2e. 2H. Humumaannm mooddeell ddeessccrriippttioionn. .
3. System Overview
3. SystemOverview
In our previous work, a conceptual model was provided to show the main steps of personality
Inourpreviouswork,aconceptualmodelwasprovidedtoshowthemainstepsofpersonality
computing, and to provide evidence for the utility of persona. However, such a model was unable to
computing,andtoprovideevidencefortheutilityofpersona. However,suchamodelwasunableto
provide enough stability in long-term running from data collection to personae generation. To
provideenoughstabilityinlong-termrunningfromdatacollectiontopersonaegeneration. Toachieve
achieve a comprehensive and stable approach to personality computing, an archetype-based personality
acomcpormehpuetninsigv seysatnemd s(AtaBbPleCa spypstreoma)c hhatso bpeeerns obnuiallti atycccoormdinpgultyi,n ags, sahnoawrnch ient yFpige-ubraes 3ed. Tpheres AonBaPliCty sycosmtempu ting
systemco(AnsBisPtCs osfy as pteemrs)onhaals dbaetae ncobllueiclttoar,c ac osrcdeninagriloy ,daesteschtoorw, anndin aF piegrusorena3l.itTyh meoAdBelPerC. Tshyes tdeemtaiclos nosf itshtes ofa
personhaeltedraotgaencoeolluesc tdoar,taa ssocuerncaersi oandde ttehcet opre,rasonndaal dpaetras oconlalelicttyorm aored einletrro.dTuhceedd einta islusbo-sfetchteiohne otenreo, gaennde ous
datastohuer fcuenscatinodn tohfe thpee rsscoennaarliod adteateccotollre cist osrhoawreni nintr soudbu-sceecdtioinn stuwbo-.s Aecst itohne coonree, paanrdt othf ethfeu pnecrtsioonnaoef the
modeler, the persona generator is demonstrated, particularly in sub-section three. Finally, sub-section
scenariodetectorisshowninsub-sectiontwo. Asthecorepartofthepersonaemodeler,thepersona
four illustrates the function of the personae modeler.
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generatorisdemonstrated,particularlyinsub-sectionthree. Finally,sub-sectionfourillustratesthe
functionofthepersonaemodeler.
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Figure 3. Archetype-based system for personality computing.
Figure3.Archetype-basedsystemforpersonalitycomputing.
3.1. Personal Data Collector
3.1. PersonalDataCollector
Personal Data is the foundation of human modeling. As mentioned in Section 2, personal data
Preefresros ntoa lthDea ktainids othf edafotau wndhaicthio rneloatfehs utom oarn ism goendeerlaitnegd. bAys am reeanl tpioenrseodn.i nFoSre ecxtaiomnp2le,, pGePrSso dnaatal data
refersfrtoomth ae skminadrtpohfodnaet ashwowhisc ah preerlasotens’st oloocartiiosng,e hneearratbteedat bryatea dreaatal pfreormso ann. iFWoartecxha rmevpelael,s Ga PpSerdsoanta’s from
asmahretaplhtho ncoensdhitoiowns, aanpde trhseo nbr’sowlosceart dioanta, fhreoamr tCbhearotmraet ecadna ntaotf oronmly taenll iuWs aat pcherrseovn’esa flasvaorpiteer swoenb’ssithee alth
types, but also shows the exact time of his/her activity surfing the internet. To describe a person’s
condition,andthebrowserdatafromChromecannotonlytellusaperson’sfavoritewebsitetypes,
condition and activity as accurately as possible, it is necessary to choose data sources that can provide
butalsoshowstheexacttimeofhis/heractivitysurfingtheinternet. Todescribeaperson’scondition
diverse personal data. Therefore, heterogeneous data sources are selected correspondingly, which
andactivityasaccuratelyaspossible,itisnecessarytochoosedatasourcesthatcanprovidediverse
comprise of a social network, wearable devices, a smartphone and ambient devices to provide the
personaldata. Therefore,heterogeneousdatasourcesareselectedcorrespondingly,whichcompriseofa
raw data that can cover the major aspects of a person’s daily life.
socialnetwAos rskh,owwena roanb ltehed elevfitc eins ,Faigsumrea r3tp, hthoen epearnsodnaaml dbaietan tcodlelevcitcoers ctoonptarionvs idtherethe efuranwctiodnast;a dtahtaa tcan
coversttrhaetifmicaatjioorn,a sdpateac tusnoifficaatpioenr saonnd’ sddaatai lyclalisfseif.ication. As raw data collected from data sources is
Ahestesrohgoewnenouosn antdh ecanle bfet uisnedF digiruecretly3 f,or tfhuerthpeer rmsoondaellindga, tthaisc doalltea csthoorulcdo fnirtsati bnes cltahsrseifeiedfu. Fnocrt ions;
data setxraamtifipclea,t itohne, GdPaSta duatnai fifrcoamti otnhea snmdardtapthaonclea sasnidfi ctahteio bnr.eaAths draataw frdomat asmcaorltl ewcatetcdh farroem coldleacttaeds oatu rces
ishettehreo gsaemnee otuimsea. nCdlecaarlny,b teheu sGePdS ddiarteac tclayn foonrlfyu irntdhiecratme oa dpeelrisnogn,’st hloiscadtiaotna, sahnodu cladn’fit rbset buesecdl afossr ified.
emotion detection. Therefore, the data stratification function is in charge of such data usage
Forexample,theGPSdatafromthesmartphoneandthebreathdatafromsmartwatcharecollectedat
classification.
thesametime.Clearly,theGPSdatacanonlyindicateaperson’slocation,andcan’tbeusedforemotion
Due to the heterogeneity of raw data, data types vary from source to source. One key problem
detection. Therefore,thedatastratificationfunctionisinchargeofsuchdatausageclassification.
is that there is no unique method to handle the processing of all the various types of data. Actually,
Duetotheheterogeneityofrawdata,datatypesvaryfromsourcetosource. Onekeyproblem
some kinds of data may involve deeper-level information that needs to be further excavated. For
isthaetxtahmerpelei,s vnaroiautinoinq iune hmeaerttbheoadt rtaoteh caonudldl ealtshoe inpdriocactees asi pnegrsoofna’sl lmthenetavla srtiaoteu, si.ety., pife tsheoyf adraet rae.laAxecdtu ally,
someork innedrvsouosf. dThautas, mthae ydaitnav uonlivfiecadtieoenp feurn-cletivoenl isi ninfoternmdaedti oton mthinaet thnee eedxtsrat oinfboermfautriothne frroemx ctahve ated.
For erxaawm dpaltea, fovra fruiratthieorn prioncehsesainrgtb, ie.ea.t, srcaetnearcioo ucoldnstarluscotioinnd aincda tpeerasopnea rmsoond’eslinmg.e Antsa tlhes tlaastte f,uin.ect.,ioinf, they
areretlhaex celdasosirfincaetrivoonu fus.nTcthiouns i,ntshieded tahtea puenrsiofincaalt idoantaf uconllcetciotonr iiss iinntteennddeedd tot obimndin theet hdeiffeexrternat itnypfoersm ofa tion
fromdthaetar awwithd athtae fsoarmfue rttihmeer psercotcioenss. inTgh,e i.dee.,tasicleedn aerxioplcaonnatsitornu cotfi otnhea nfudnpcteirosnosn farommo dpeelrisnogn.aAl dsatthae last
collector is drawn in the next section.
function,theclassificationfunctioninsidethepersonaldatacollectorisintendedtobindthedifferent
typesofdatawiththesametimesection. Thedetailedexplanationofthefunctionsfrompersonaldata
3.2. Scenario Constructor
collectorisdrawninthenextsection.
In Human-Computer Interaction (HCI), scenario refers to the context of an activity so that it can
3.2. Sbceen darisiocuCsosends trfourc tsoyrstem refinement. For example, chatting with friends on WeChat or sharing
something on Moments could be regarded as two different scenarios in terms of WeChat APP.
InHuman-ComputerInteraction(HCI),scenarioreferstothecontextofanactivitysothatitcanbe
Obviously, the scenario used for HCI, also named an application-oriented scenario, could separate
discutshsee dusfeorr ascytisvtietmiesr oenfi na ecmeretanitn. aFpoprleicxaatimonp line,toc hseavtteirnagl swituitahtiforniesn. HdsowonevWere, Cthhea stcoenrasrhioa riinn tgersmoms oeft hing
on Mpoemrseonnatslitcyo cuolmdpbuetinregg maradineldy ahsast wtwoo dfiefafteurreens.t Fsicresntlya,r itohse ihnetteerromgesneoofuWs deCatha astouArPcePs. iOn btvhiiso usly,
the screesneaarricoh uguseadranfotere HthCatI ,daaltaso isn gaamtheedreda nina dpipvelircsae twioany-so, rii.ee.n, tferodms cweenaarraibol,esc, osumladrtspehpoanreast eort thhee user
Sensors2018,18,684 7of25
activitiesonacertainapplicationintoseveralsituations. However,thescenariointermsofpersonality
computingmainlyhastwofeatures. Firstly,theheterogeneousdatasourcesinthisresearchguarantee
thatdataisgatheredindiverseways,i.e.,fromwearables,smartphonesortheinternet,sothatthe
scenarioisabletoinvolvethehumanstateinallaspectsoflifebutnotexclusivelyasactivitieson
asingleHCIsystem. Therefore, thefunctionscenarioconstructioninsidethescenariodetectoristo
gather all the possible data according to the person’s activity. The key point is how to ensure the
datahighlyrelatedtoaspecificscenario. Secondly,themaingoalofthescenarioconstructioninthis
researchistocategorizetheenvironmentforthementalstatemodelingofaspecificsubject. Sucha
scenarioisnamedthepersonality-orientedscenario. Thus,thescenarionormalizationfunctioninsidethe
scenariodetectoristonormalizethepersonaldataintoseveralaspects,i.e.,time,locationandactivity,
forfurtherpersonalitycomputing. Theprocessofscenariodetectionandkeymethodsforscenario
normalizationarediscussedinSection5.
3.3. PersonaDetector
AsdefinedbySwisspsychiatristCarlJung,apersonawasthestablesocialfacetheindividual
presented to the world. Persona is similar to a role, which describes each human’s psychological
statusinintermsoftheperson’sinteractionwithsociety. Forexample,apersonmaypresentdifferent
personaewheninteractingwithdifferentpeople,i.e.,chattingwithparents,goingoutwithfriends,
orworkingwiththeirboss. Wenormalizedtheinteractionwithothersintoseveralkindsofscenarios.
Therefore, such mental variation is the unconscious performance by our inner mind in different
scenarios. Toresearchdeepintoaperson’sinnermind,onestraightforwardwayistoresearchhis/her
persona. However, the persona currently is simply a psychological description and hasn’t been
digitallymodelizedyet. Thepersonaasunderstoodinpsychologyisdifficulttouseinpersonality
computing. Therefore,themaintargetofpersonadetectoristodigitalizethepsychologicalpersonainto
acomputabledigitalpersonaform.
Asweknow,humansarecomplexandmaypresentadifferentpersonaineachdifferentscenario.
Hence, to digitalize the persona, the core issue is to seize on the invariable features present in a
human’ssocialactivity. AccordingtotheJung’stheoryofpersonality,archetypes,whichconsistof
twelve different aspects act as the prototype of a human’s psychological pattern and exist within
thehumanmind. Actually,ahumanmaypresentdifferentarchetypesindifferentscenarios. Hence,
thearchetypecharacterizationfunctioninsidethepersonageneratorisneededforthefurtherpersona
modeling. Adetailedexplanationofarchetypecharacterizationandpersonaconstructionisexplained
inSections6and7,respectively.
3.4. PersonalityModeler
Although a person may present different personae in the different scenarios, for a mature
individual, his/her persona in various scenarios is relatively stable. The Personality modeler is to
depict a person according to all his personae present in different scenarios. Firstly, to describe a
particularperson,thecharacterizationofpersonaewithdifferentaspectsisnecessary. Forexample,
whentogetherwithfriends,apersoncouldbesummarizedasanhonestpersonwhoiswillingtohelp
othersbutalsowantstobeahero. Hence,thefacetpersonaemodelingfunctioninsidethepersonality
modeleristocharacterizeaperson’spersonaduetosomeuniversalfacet,i.e.,people,activity,etc.
Secondly,thepersonalitycomputingistogiveasummaryofapersonaccordingtoallhisfacetpersonae.
ThedetailofmodelingofpersonalitymodelerisexplainedinSection8.
4. PersonalDataCollection
Inthissection,heterogeneousdatasourceswithtwofeaturesarediscussedinthefirstsub-section.
Due to such duality of features, the main step of data collection is proposed accordingly, and is
explained in the second sub-section. As the steps of data collection, personal data stratification,
personaldataunification,andpersonaldataclassificationareclarifiedinthelastthreesub-sections.
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4.1. HeterogeneousDataSources
WiththepopularityoftheInternetandInternetofThings(IoT),moreandmorepersonaldata
isaccessible. Inthisresearch,thesmartphone,wearabledevices,ambientdevices,andtheInternet
areselectedtoprovideplentyofdataforpersonalitycomputing,andTableS1showsallthepersonal
datatypesandtheirsources. Duetotheirheterogeneity,threefeaturesofthesedatasourceshindered
scenarioconstructionandpersonamodeling. Thesethreefeaturesofheterogeneousdatasourcesare
Sensors 2018, 18, x FOR PEER REVIEW 8 of 25
shownbelow.
selected to provide plenty of data for personality computing, and Table S1 shows all the personal
• Dynamdaitca Dtyapteas aAnvda tihlaeibri sloituyr:ceFso. Druaep toa trhteiciru hleatrertoygpeneeoitfy,p therreseo fneaatlurdeas toaf ,ththesee advataai sloaubricleitsy hiinsdleirmedi tedbythe
scenario construction and persona modeling. These three features of heterogeneous data sources are
user’sbehavior,user’slocation,andthedevicebattery. Therefore,thescenarioconstructionand
shown below.
personamodelingfunctionarefacingthelimitationsofdynamicdatasources.
 Dynamic Data Availability: For a particular type of personal data, the availability is limited by the
• MultipleDataSources: Foraparticulartypeofpersonaldata,thedataisprovidedbymultiple
user’s behavior, user’s location, and the device battery. Therefore, the scenario construction and
sources,spuecrhsoansa tmhoedsemlinagr ftupnhcotinone aarne dfacsimnga trhtew liamtcithatbioontsh ofp dryonvaimdiicn dgaGtaP soSudrcaetsa. .Therefore,theselection
ofthe appMruolptiprliea Dteatda aStoaurscoesu: rFcoer ais pnareteicdueladr .type of personal data, the data is provided by multiple
sources, such as the smartphone and smartwatch both providing GPS data. Therefore, the
• MultilevelDataSources:Thedatalevelvariesfromtypetotype.Forexample,thesmartwatchcould
selection of the appropriate data source is needed.
provid ehMeaurlttilervaetle Ddataat aSoeuvrceesr:y Tmhei ndauttae l,eavenld vasruiecsh frdomat atycpoeu told typpreo. vFoidr eexaanmpalcec, uthrea tsemparetwrsaotcnha lemotion
recognitioconu.ldH porowveidvee hre,atrht erastele deapta devaetray pmrionvutied, eadndb syucah sdmataa rcotpulhdo pnroevwidhe iacnh acjucusrtatien pdeircsaotneasl theusers’
sleepconedmitoitoionn hreocougrnlyitiocna.n Hoonwleyvebr,e thues seldeepfo draatap pprroovixdiemd abtye ap semrasrotpnhaolnae cwtihvicihty jurset cinodgincaitteiso n. Hence,
the users’ sleep condition hourly can only be used for approximate personal activity recognition.
onlypartofthedataispreparedforscenarioconstruction,andotherpartsforpersonamodeling.
Hence, only part of the data is prepared for scenario construction, and other parts for persona
modeling.
4.2. FlowofPersonalDataCollection
4.2. Flow of Personal Data Collection
Duetodatasourcesdynamicallychanging,collectedrawdatashouldbeprocessedfrequently
Due to data sources dynamically changing, collected raw data should be processed frequently
whenusedforfurtherscenarioconstructionandpersonamodeling. Thedatacollectionflowisshown
when used for further scenario construction and persona modeling. The data collection flow is shown
inFigure4.
in Figure 4.

Figure 4. Data collection flow.
Figure4.Datacollectionflow.
The data collection mainly consists of three sub-functions. They are data stratification, data
The duantifaicactoiolnle, catnido dnatma calainssliyficcatoionns.i Dstuse otof ththe rdeyenasmuibc -cfhuanngcet iionvnosl.veTd hine tyhea draetad saoutarcesst raantdi fialcsao tion, data
the diversity of data types, the ABPC system is unable to categorize the exact type and quantity of
unification,anddataclassification. Duetothedynamicchangeinvolvedinthedatasourcesandalso
collected personal data in advance. Taking advantage of such raw data for further modeling without
thediversityofdatatypes,theABPCsystemisunabletocategorizetheexacttypeandquantityof
any processing may lead to inadequate or even incorrect data utilization. For example, the step data
collectedpperorsvoidneadl bdya stmaairnt sahdoevsa cnocueld. Teaaskiliyn dgesacdrivbae na tpaegresoon’fs swuaclhkinrga wspedeadt aanfdo trhfeu srtethp efrreqmuoendceyl ionf gwithout
anyprocesthseinir ggamit athyanlekas dto ttohei nhiagdh eqquualaittye oof rtheev deanta ianncdo ar rseucffticdieantta duattai lsiazmatpiloinng. fFreoqrueenxcaym. Hpolwee,vthere, stepdata
the data quality will deteriorate, and such step data only indicate that a user is moving or not during
providedbysmartshoescouldeasilydescribeaperson’swalkingspeedandthestepfrequencyof
periods of time when the smart shoes are not available, and the only data source is a smartphone.
theirgaitthankstothehighqualityofthedataandasufficientdatasamplingfrequency. However,
To reduce incorrect data utilization, an initial data classification is necessary. Hence, the data
thedataqustaraltiitfyicawtioilnl ids etote sreipoarraattee t,haen ddatas uinctho tshtreepe ldayaetras aocncolyrdiinngd tioc athtee ttehmaptoaraul rsaewr idsatma toyvpien, sgo othrant otduring
periodsofdtiifmfereenwt dhaetan utthileizastmiona rfutnschtiooenss, ai.ere., snceontaarivo aciolnasbtrluec,taionnd antdh peeorsnolnya dmaotdaelsinogu, rcacne uissea disfmferaernttp hone.
layer of data. To reduce inadequate data utilization, data unification is developed accordingly. The
Toreduceincorrectdatautilization, aninitialdataclassificationisnecessary. Hence, thedata

stratificationistoseparatethedataintothreelayersaccordingtothetemporalrawdatatype,sothat
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differentdatautilizationfunctions,i.e.,scenarioconstructionandpersonamodeling,canusedifferent
layer of data. To reduce inadequate data utilization, data unification is developed accordingly.
Sensors 2018, 18, x FOR PEER REVIEW 9 of 25
Themainpurposeofdataunificationistofirstmineonelayerofdata,andthengeneratenewdatain
differentmlaayienr ps.urSpuocshe oaf pdraotcae usnsifiiscantaiomn eisd ttoh feirsset cmoinnde odnaet alacyoelrl eocf tdioanta., Faninda tlhlye,nt hgeenderaattae cnleawss idfiactaa tiino nwill
classifythdeiffderaetnat ilnatyoertsw. Sougchr oau pprsocfeosrs sics ennaamreiod cthoen ssetrcuonctdi odnat(aS cCo)lleacntidonp. eFrisnoanllay, mthoed dealtian gcla(sPsMific)asteiopna rately.
will classify the data into two groups for scenario construction (SC) and persona modeling (PM)
Thedetailofthreesub-functionsisexplainedinthefollowingthreesub-sections,respectively.
separately. The detail of three sub-functions is explained in the following three sub-sections,
respectively.
4.3. PersonalDataStratification
Bein4g.3.t hPeersfiornsatl Dpartoac Setsrastiofifcadtiaonta collection,thedatastratificationfunctionclassifiesthedatainto
threelayers,aBneidngi ttshde efimrsto pnrsotcreastsi oonf disatsah coowllenctiionnF, tihgeu dreat5a .stratification function classifies the data into
three layers, and its demonstration is shown in Figure 5.

FigurFeig5u.reD 5e. mDeomnsotnrsatrtaiotinono offd daattaa ssttrraatitfiificactaiotino. n.
The main use of collected personal data is for further scenario construction and persona
Themmoadienliungse. Dofuceo tlole cthteed dpifeferrseonncaesl dina tathies efolermfeunrttahl ecrosmcepnosairtiioonc oonf sstcreuncatriioon aanndd ppeersrosnoan, athmeoird eling.
Due to threequdirifefmereenntsc efosr ipnertshoenaell edmatae natrael ccoorrmesppoonsidtiinognlyo dfisffceerennatr. iCooanncrdeteplye,r ssocennaa,ritoh ecoirnsrteruqcutiiorne ments
needs data that indicates the basic elements of a scenario, i.e., time, location, type of activity and who
for personal data are correspondingly different. Concretely, scenario construction needs data that
is involved. Hence, such data, named scenario data, consists of the top layer of personal data. In
indicatesthebasicelementsofascenario, i.e., time, location, typeofactivityandwhoisinvolved.
contrast to the scenario data, the state data is for the comprehensive description of a person’s state
Hence, such data, named scenario data, consists of the top layer of personal data. In contrast to
within a concrete scenario, as shown in the center of Figure 5. Because scenario data is limited to
the scenasrhioowdinagt ath,et hroeugstha atcetidviatyta tyipsef, oi.re.,t hwealckoinmg,p prleahyienngs, iovr echdaettsincgri wptiitho nothoefrsa, spuecrhs doant’as iss tnaotte riwchi thin a
concreteesncoeungahr itoo ,parosvsidheo dwesncriinpttiohne ocf ean pteerrsoonfaFl sitgauter.e H5e.ncBe,e sctaautes deastac econnatraiionsd aa ptearsiosnl’ism reictoerddst oof sahllo wing
theroughof athctei vstiattyest aynpde i,tsi. ev.a,rwiataiolknsin ing ,teprmlasy oinf pgh,yosrioclhogayt,t ienmgowtiointh, aondth beerhsa,vsiuorc. hCodnasteaquisenntolyt, rdiucrhinegn ough
the same scenario, the differences in state data from person to person reflect the uniqueness of the
to provide description of a personal state. Hence, state data contains a person’s records of all of
individual. Aside from the two kinds of data above, other kinds of data are classified into the raw
the states and its variations in terms of physiology, emotion, and behavior. Consequently, during
data, as is shown in the bottom layer of Figure 5. Raw data is data that cannot obviously indicate the
thesamescenario,thedifferencesinstatedatafrompersontopersonreflecttheuniquenessofthe
scenario elements or human state. However, by further digging, such data may indicate some hidden
individuianlf.oArmsaitdioenf wrohmicht hcoenttrwiboutkesin tod sthoe fscdeantaarioa bdoatvae a,nodt hsteartek diantad.s Foorf edxaamtapaler,e thcel avsosiicfie eredcoinrdtso otnh eraw
data,asias psherosownn’s isnmtahrtephboontteo mmayl abye edretoefctFeidg buyr em5a.chRinaew-ledaarntainigs, adnadt amtahya itdecnatnifnyo at poebrsvoino uors lpyeoipnlde iicna tethe
scenarioethlee mimemnetsdioarteh vuicminaitny osft athtee .smHaorwtpehvoenre, cbayrrifeurr. tThheer pdroigcegsisn ogf ,osbutacihnindga tthaem scaeynairniod dicaatate ansodm staeteh idden
data by mining this raw data is called personal data unification. The details of personal data
informationwhichcontributestothescenariodataandstatedata. Forexample,thevoicerecordson
unification are explained in the next sub-section.
aperson’ssmartphonemaybedetectedbymachine-learning,andmayidentifyapersonorpeople
intheim4m.4e. Pdeirastoenavl iDciantai tUynoififcathtioens martphonecarrier. Theprocessofobtainingthescenariodataand
statedatabyminingthisrawdataiscalledpersonaldataunification. Thedetailsofpersonaldata
During the data collection, the majority of collected data is regarded as raw data, and only a few
unificatiodnataar tehaetx cpolnatianine dthien atphpearnenext tscseunbar-isoe dctaitoan o.r human state data can be classified into the top layer
and second layer in Figure 5. Hence, as mentioned in the last sub-section, personal data unification
4.4. PersomnainleDs athtae hUidndifiecna itniofonrmation from the raw data as thoroughly as possible and unifies the data with
the requirements of scenario construction and persona modeling. Two different methods are
Duringthedatacollection,themajorityofcollecteddataisregardedasrawdata,andonlyafew

datathatcontaintheapparentscenariodataorhumanstatedatacanbeclassifiedintothetoplayer
andsecondlayerinFigure5. Hence,asmentionedinthelastsub-section,personaldataunification
Sensors 2018, 18, x FOR PEER REVIEW 10 of 25
provided for personal data unification, namely scenario data unification and state data unification.
The core method of these two unifications relies on calculating the correlation between the specific
personal part of raw data and the target data. Then such chunks of raw data can be used for target
data verification, or even for data mining. One thing that should be clarified is that the correlation
between raw data and target data varies from person to person. Therefore, the correlation will be
calculated specifically to ensure accurate results and full usage of the raw data in each subject’s case.
Sensors2018,18,684 10of25
4.5. Personal Data Classification
mineAstlthheohuigdhd ethnei ndfaotram isa tsieopnafrraotemd tihnetor atwhredea tlaayaesrsth boyr otuheg hdlaytaa sstproastisfiibclaetiaonnd fuunncifitieosnt, htoe danataalywzeit ha
tpheersroeqnu’si rsetmateen ints ao fcescrteanianr isocecnoanrsitor,u acnti oanssaoncdiaptieorns obneatwmeoedne slcinegn.aTriwo oddatiaff earnedn tsmtateet hdoadtas aisr enpecroesvsiadreyd.
fCoornpcerrestoelnya, ltdhaet astuatnei fidcaattai osnh,onualmd ebley wsceeingahrtieodd aactacourndiifingca ttoio nthaen sdcesntaatreiod aat apeurnsiofinc aitsi oinnv.oTlhveedc oirne.
mTheethreofdoroef, tthhees epetwrsoonuanli fidcaattai ocnlassrseilfiiecsatoinonc aalsc uthlaet ifningatlh peacrotr roefl adtaiotan cboeltlweceteionnt hbeinsdpse ctihfiec spceernsaorniaol dpaatrat
oanfdra wstadtea tdaaatnad atchceortdarignegt tdoa ttah.eT shceennasruicoh eclehmuneknstso. fFroarw edxaamtapclaen, btheeu ssteadtef odrattaar gweotudladta bvee crilfiascsaitfiioend,
oacrceovredninfgo rtod adtiaffemreinnitn tgim. Oe naendth lioncgattihoant. sOhnoeu ltdhibneg ctlhaarti fisehdouisldt hbaet etmhepchoarsriezleadti oisn thbaett wtheee ntimraew fadcatotar
aisn idndtairsgpeetndsaabtalev. aFroier sefxraommpplee,r tshoen ptoerpsoernsaoln d.aTtha ewreofuolrde, bteh eclcaosrsriefileadti oanccworidllibneg ctaol cliunlkaetedd faspcteocrisfi csualclhy
taos etinmsue raenadc cloucraattieorne,s ourl ttsimaned anfudl laucstiavgietyo. ftherawdataineachsubject’scase.
45..5 S.cPeenrsaornioa lNDoartmaCalliazsasitfiiocant io n
AAlst hdoeusgcrhibtehde dina taseicstsioenp atrharteeed, ian tsoctehnraereiola syeerrvsebsy atsh ea dbartiadgster aftoifir cmatoiodnelfiunngc tpioerns,otnoaalintyal yfrzoema
pheertesroong’sensteaotuesi npearcseorntaali ndsactean. aArnio o,avneravsiseowc ioatfi oscnebneatrwioe denetseccetinoanr iios dseatt aoaunt dins ttahtee ndeaxtat issunbe-sceecstsiaorny..
CExoancctrleyt ealsy ,shthoewsnt ainte Fdigautares h6o, uscldenbaeriow eeliegmhteendtsa dcceoscrrdiipntgionto, tthhee psrcoecneasrsi oofa spceenrsaornio insoirnmvoallvizeadtioinn.
Tanhder ietfso kreey,t mheeptheordsosn aarle dilalutastcrlaatsesdifi sceaptiaornataeslyt hine tfihnea llapsta trhtroefed sautba-csoecllteioctniso.n bindsthescenariodata
andstatedataaccordingtothescenarioelements. Forexample, thestatedatawouldbeclassified
a5c.1c.o Drdeisncgriptotiodnif ofef rSecnetntairmioe Nanordmlaolcizaatitoionn. Onethingthatshouldbeemphasizedisthatthetimefactoris
indispensable. Forexample,thepersonaldatawouldbeclassifiedaccordingtolinkedfactorssuchas
Figure 6 provides an overview of scenario normalization. The labels of scenario elements are
timeandlocation,ortimeandactivity.
shown on the left in Figure 6. They are location, social group, and activity. In the middle of Figure 6,
each small square refers to a scenario element corresponding to its label on the left. From left to right,
5. ScenarioNormalization
array of squares is arranged in time order. For a certain square array, for example, a square with a
As described in section three, a scenario serves as a bridge for modeling personality from
different color refers to a different activity, and the width of each square represents the duration of
heterogeneouspersonaldata. Anoverviewofscenariodetectionissetoutinthenextsub-section.
such activity according to the personal data. The scenario normalization function detects and
ExactlyasshowninFigure6,scenarioelementsdescription,theprocessofscenarionormalizationand
generates a set of normalized scenarios from the scenario data over a period of time. The formulation
itskeymethodsareillustratedseparatelyinthelastthreesub-sections.
of scenario elements is illustrated in the next sub-section.

Figure 6. Overview of scenario normalization.
Figure6.Overviewofscenarionormalization.
5.2. Scenario Element Description
5.1. DescriptionofScenarioNormalization
In this sub-section, the location, social group and activity are clarified separately with the
Figure6providesanoverviewofscenarionormalization. Thelabelsofscenarioelementsare
following formulae.
shownontheleftinFigure6. Theyarelocation,socialgroup,andactivity. InthemiddleofFigure6,

eachsmallsquarereferstoascenarioelementcorrespondingtoitslabelontheleft. Fromlefttoright,
arrayofsquaresisarrangedintimeorder. Foracertainsquarearray,forexample,asquarewitha

differentcolorreferstoadifferentactivity,andthewidthofeachsquarerepresentsthedurationofsuch
activityaccordingtothepersonaldata. Thescenarionormalizationfunctiondetectsandgeneratesa
setofnormalizedscenariosfromthescenariodataoveraperiodoftime. Theformulationofscenario
elementsisillustratedinthenextsub-section.