Mobile Data Mining for Intelligent Healthcare Support

Transcription

1 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces Moble Daa Mg for Iellge Healhcare uppor Par Delr Haghgh, Arkady Zaslavsky, hoal Krshaswamy, Mohamed Medha Gaber Ceer for Dsrbued ysems ad ofware Egeerg Moash Uversy, Ausrala {par.delrhaghgh, arkady.zaslavsky, shoal.krshaswamy,, mohamed.gaber}@foech.moash.edu.au Absrac The growh umbers ad capacy of moble devces such as moble phoes coupled wh wdespread avalably of expesve rage of bosesors preses a uprecedeed opporuy for moble healhcare applcaos. I hs paper we propose a ovel approach for uao-aware Adapve Processg (AAP) of daa sreams for smar ad real-me aalyss of daa. The mplemeao ad evaluao of he framework for a healh moorg applcao s descrbed.. Iroduco Recely, ovaos moble commucaos ad low-cos of wreless bosesors have paved he way for developme of moble healhcare applcaos ha provde a covee, safe ad cosa way of moorg of val sgs of paes. A key he provso of moble healhcare servces s he ssue of usg echologcal ovao o suppor couous moorg of pae codos, provdg a degree of self-dagoss ad eablg effecve real-me decso makg o reduce faales. Ubquous Daa ream Mg (UDM) echques [] such as lghwegh, oe-pass daa sream mg algorhms [2-3] ca perform real-me aalyss o-board small/moble devces whle cosderg avalable resources such as baery charge ad avalable memory. However, o perform smar ad ellge aalyss of daa o moble devces, s mperave for adapao sraeges o facor coexual formao. Coexual formao ca be relaed o a ework, applcao, evrome, process, user or devce. As a mea-level cocep over coex we defe he oo of a suao ha s ferred from coexual formao [4]. uao-awareess provdes applcaos wh a more geeral ad absrac vew of her evrome raher ha focusg o dvdual peces of coex. uao-aware adapve daa sream mg leverages he full poeal of UDM by gog beyod mere avalable resources ad ca eable, f o guaraee, he couy ad cossecy of he rug applcaos. I real-world, suaos evolve ad chage o oher suaos (e.g. healhy chages o hypereso). Chages ha occur bewee suaos are also good dcaors of suaos ha may emerge albe wh some vagueess ad uceray. To eable suao-awareess moble healhcare applcaos, s mpora for he suao modelg ad reasog approach o represe uceray ad vagueess assocaed wh healh-relaed suaos. Revewg rece works moble healhcare reveals ha mos of hese proecs [5-8] have maly focused o usg, ehacg or combg exsg echologes ad coex-aware proecs [9-3] mosly deal wh a lmed scope (.e. o applcable o oher coex-aware scearos). I moble healhcare compug, a geeral approach for modelg ad reasog abou ucera, healh suaos ad performg smar ad cos-effce aalyss of daa real-me has o bee roduced ad s a ope ssue. I hs paper we propose suao-aware adapve processg (AAP) of daa sreams for moble healhcare applcaos. The ovely ad corbuo of hs proec are as follows: ) suao-awareess s acheved by Fuzzy uao Iferece (FI) ha combes fuzzy logc prcples wh he Coex paces (C) model, a formal ad geeral coex modelg ad reasog for pervasve compug evromes. The sreghs of fuzzy logc for modelg of vague suaos are combed wh he C models uderlyg heorecal bass for supporg coex-aware pervasve compug scearos; ) AAP corporaes suao-awareess o daa sream mg ad provdes gradual ug of daa sreamg parameers accordg o occurrg suaos ad avalable resources. Ths approach mproves daa sream mg operaos a ellge ad coseffce maer. The AAP approach eables couy ad cossecy of rug operaos ha are of hgh mpora for healh moorg applcaos ha deal wh sesve ad crcal daa /09 $ IEEE

2 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces A cearo Joh has had a hear aack ad s released from hospal bu here are cocers ha he mgh be suscepble o aoher hear aack ad s also experecg blood pressure flucuaos. Cosa moorg of hs val sgs could help hm o reduce hs axey, decrease he eed for roue vss o medcal facles, ad also deec early warg feaures of a possble mpedg eve. He has a smar phoe wh AAP salled o ad s wllg o wear bosesors o measure hs val sgs. The daa s wrelessly se o hs moble where AAP deecs ay chages o oly hs val sgs bu ay coexual formao ha s relaed o he applcao (e.g. he baery level of he moble phoe). AAP uses hs formao o reaso abou suaos real-me ad accordg o ferred suaos, performs ellge ad cos-effce aalyss of daa. Whe flucuaos of val sgs are wh a specfed accepable hreshold, here s o eed for freque measureme ad use of resources ca be reduced ad moderaed. However, whe hese flucuaos are over he hreshold, hs suao warras a closer moorg by he sysem ad more freque measuremes. Ths ype of adapao requres facorg boh avalable resources ad crcaly of healh suaos. Ths paper s srucured as follows: eco 2 dscusses he relaed work. eco 3 preses he AAP archecure. eco 4 descrbes he Fuzzy uao Iferece (FI) ha eables suaoawareess. eco 5 dscusses he adapao ege. eco 6 ad 7 descrbes mplemeao ad evaluao respecvely. Fally seco 8 cocludes he paper ad dscusses he fuure work. moble healh moorg applcaos ha has bee suded [8]. Coex-awareess s oe of he key requremes of healh moorg sysems ha eables auoomous operaos whou paes erveo ad ehaces decso makg of healhcare professoals o pae codo [9]. However, here are lmed researches ha have aemped o fully address he coexawareess or provde a geeral ad formal represeao of coex [0-2]. Oe of he works moble healhcare ha corporaes boh coexawareess ad adapao s proposed [3] bu he paper does o provde he deals of how ad whe he proposed adapao sraeges are appled. udes daa sream processg [4-5] are very applcaospecfc ad focus o very lmed areas of research. A geeral approach for smar ad cos-effce aalyss of daa for moble healhcare sysems has o bee roduced he curre sae-of-he-ar ad s sll a ope ssue. 3. uao-aware Adapve Processg (AAP) of Daa reams The archecure for uao-aware Adapve Processg (AAP) of daa sreams cosss of hree compoes of Fuzzy uao Iferece (FI), Resource Moor (RM) ad Adapao Ege (AE) as show Fgure. 2. Relaed Work Moble healhcare compug s a ew ad evolvg area of research ha explos he rece developme moble eworks ad commucaos for healh moorg applcaos. EPI-MEDIC [5] s a large scale Europea proec ha provdes persoal moorg of ECG sgals for early deeco of cardac schema ad arrhyhma ad geerag dffere levels of alarms. Aoher Europea proec called he MobHealh proec [6] uses 2.5 (GPR) ad 3G (UMT) echologes o egrae all he sesors ad acuaors o a wreless ework called Body Area Nework (BAN). The proec of ubmo (Ubquous Moorg Evrome for Wearable ad Implaable esors) [7] ams o provde couous maageme of paes maly focusg o sesors ad wreless echology raher ha daa aalyss echques. Persoalzao s aoher area of focus developg Fgure. The archecure of AAP (uao- Aware Adapve Processg) of Daa reams The FI ege eables suao-awareess usg fuzzy logc prcples. Resource Moor (RM) s a sofware compoe ha couously moors avalable resources such as avalable memory ad baery usage ad repors her avalably o he adapao ege. The Adapao Ege (AE) s resposble for gradual ug of daa sream processg parameers real-me accordg o he occurrg suaos ad avalable resources. The 2

3 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces AAP layer s bul o he op of he daa sream mg algorhms rug o moble devces ad provdes hem wh suao-aware adapao. The ex seco dscusses he FI echque. 4. Fuzzy uao Iferece (FI) FI s a suao modelg ad reasog approach ha egraes fuzzy logc o he Coex paces (C) model [4]. FI uses he beefs of he C model for supporg pervasve compug evromes whle corporag fuzzy logc o deal wh uceray assocaed wh vague ad real-world suaos. 4.. The Coex paces model The Coex paces model (hereafer C) represes coexual formao as geomercal obecs muldmesoal space called suaos [4]. The cocep of a suao space s characerzed by a se of regos. Each rego s a se of accepable values of a coex arbue ha sasfes a predcae. I addo o basc coceps ad echques for suao modelg ad reasog, he C model provdes heurscs developed specfcally for addressg coex-awareess uder uceray. These heurscs are egraed o reasog echques ha are uly-based daa fuso algorhms ad compue he cofdece level he occurrece of a suao [6]. The C deals wh uceray maly assocaed wh sesors accuraces. Ye here s aoher aspec of uceray huma coceps ad real-world suaos ha eeds o be represeed by he coex model ad refleced he resuls of suao reasog. Fuzzy logc uses mul-value logc ad has he beef of dealg wh hs level of uceray by assgg membershp degrees o values uao Modelg FI cosss of hree subcompoe cludg fuzzfer, rules ad ferece ege. Fuzzfer, as a sofware compoe, maps crsp pu (.e. values of coex arbues) o fuzzy ses usg rapezodal membershp fucos. I a fuzzy se, membershp of a em s gradual ad s represeed by a degree bewee 0 ad [7]. I FI, suaos of eres are defed usg fuzzy rules by doma expers ad sored a rule reposory. Each FI rule cosss of mulple codos oed wh he AND operaor bu a codo ca self be a dsuco of codos [8]. To model he mporace of codos, we assg a wegh w o each codo wh a value ragg bewee 0 ad. The sum of weghs s per rule. A wegh represes he mporace of s assged codo relave o oher codos defg a suao. A example of a FI rule s as follows: f Room-Temperaure s ho ad Hear-Rae s fas ad ( Age s mddle-aged or old) he suao s hea sroke The ex subseco dscusses suao reasog uao Reasog To reaso abou a suao, rules eed o be evaluaed o produce a sgle oupu ha deermes he membershp degree of he coseque [9]. The codos oed wh he OR operaor are evaluaed usg he maxmum fuco. However, o evaluae he codos oed wh he AND operaor, FI provdes four reasog echques as show Table. Table. Reasog echques Heursc: weghs ad corbuo level C Cofdece = w c = FI Cofdece = w μ ( x ) = Heursc: sesors accuracy C Cofdece = w. Pr( a A ) FI = Cofdece = w μ ( f ( x, e = Heursc: symmerc ad asymmerc arbues C Cofdece = w.pr( a A ) FI where = a CA CA Cofdece = w μ ( f ( x, e = where x F ad F LV LVA Heursc: paral ad complee coame C m Cofdece = q w. p( a A ) + q p( a A FI A )) )) 2 k k ) = k= + q2 = where q ad a CA CA, a CA A = + q2 = k Cofdece = q w. μ( f ( x, e ) + q μ( f ( xk, ek )) where q ad x F, F LV LVA x k F, F LV m 2 k = 3

4 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces These echques egrae fuzzy logc o he C reasog mehods o provde aoher aspec of uceray (.e. uceray of suaos ad dela chages of coex) he compuao of cofdece value for he occurrece of a suao. The suao reasog echques of C are based o four heurscs ha are roduced o maage uceray pervasve compug evromes. These heurscs are as follows: ) relave weghs of coex arbues ad cofdece level of values; ) sesors accuracy; ) symmerc ad asymmerc coex arbues; v) ad paral ad complee coame of symmerc coex arbues. Table depcs reasog mehods of C, her FI equvale ha are combed wh fuzzy logc ad her uderlyg heurscs ad heorecal coceps. The ex subsecos dscuss each heursc ad reasog echque more deal Weghs ad corbuo level. The frs reasog echque of C s based o he weghs of coex arbues ad he level of cofdece of arbues values. Weghs are assged o coex arbues ad represe relave mporace of each coex arbue for ferrg a suao. Level of cofdece s assged o each eleme ad reflecs how ha eleme relaes o he modeled suao. I hs heursc, he corbuo fuco ha compues he corbuo level s proposed a a cocepual level ad s mplemeao s laer roduced he secod reasog echque based o sesors accuracy. I FI, he cocep of weghs s assocaed wh lgusc varables (.e. coex arbues). The cocep of corbuo level s smlar o he membershp degree of elemes a fuzzy se bu hey are mplemeed usg membershp fucos. The resul of w μ ( x ) represes a weghed membershp degree of x ad represes he umber of codos a rule () esors accuracy. To provde auomac compuao of he corbuo level a ru-me, he secod reasog mehod of C uses he mpac of sesor accuraces ad urelably as a deermg facor o compue he corbuo level. Ths mehod compues he probably of a coex arbue correc value a beg coaed he rego A. To compue he probably value based o he relably of a sesor, he relably of readg (e.g. 95%) s used o represe he probably value (.e. =0.95). The secod opo o compue he probably value s o egrae he sesors accuracy of readg raher ha he relably of readg. Usg hs opo, he probably value s calculaed he followg forma: ) Pr( e a m( A )) Pr( e a max( A )). where a deoes he sesed value of he coex arbue, e represes he sesor readg error (.e. a - a ) ad m( ) A ad max( A ) represe mmum ad maxmum values of he rego. The secod reasog mehod of C deals wh uceray facorg accuraces of sesors however hs equao does o reflec dela chages of values he equao ad s o adequae o reaso abou vague suaos. The FI equvale echque o oly corporaes he corbuo level assocaed wh sesors accuracy bu cludes he membershp of he values as aoher facor affecg he corbuo level. I he FI model, we frs calculae he correc value based o he relably or error rae ad he pass o he membershp fuco. The fuco f calculaes he correc value of he coex based o he accuracy value e. If e s a relably rae, he sesed value s mulpled by ad f s a error rae (.e. ±) s added o he sesed value ymmerc ad asymmerc coex arbues. The hrd reasog echque of C roduces he coceps of symmerc coex arbue CA ad asymmerc coex arbue CA A. A symmerc coex arbue creases he cofdece ferrg a suao f s value s wh he correspodg rego ad decreases he cofdece f s ousde ha rego (e.g. reasog abou he hypereso suao based o blood pressure). A asymmerc coex arbue creases he cofdece ferrg a suao f s value s wh he correspodg rego bu would o decrease he cofdece f s ousde ha rego (e.g. reasog abou he hea sroke suao based o age). Wheever a asymmerc arbue s o coaed wh s rego, he redsrbuo mehod assgs 0 o he wegh of he arbue ad recalculaes he relave weghs for he remag arbues as follows. 2) w = w / w = The cocep of symmerc ad asymmerc arbues ad s correspodg reasog echque s appled o FI (as show Table ). However, sce 4

5 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces values ha lgusc varables ake are o umerc (.e. hese values are called erms ha represe fuzzy ses), he cocep of symmerc ad asymmerc coceps are appled o he values of fuzzy ses assocaed wh lgusc varables Paral ad complee coame. The fourh heursc deals wh he fac ha he value of a mpora coex arbue should affec he resul of he suao ferece more ha he oher arbues (.e. less mpora oes) ad whe several arbues are sgfca for he evaluao of a suao we may wa o esure ha all of hem are coaed her regos. Ths heursc has bee egraed o he fourh reasog echque ha ams o address he rade-off bewee complee coame of all symmerc coex arbues (.e. whe all values of symmerc arbues are coaed her correspodg regos) ad her dvdual corbuo usg he hrd reasog echque. Ths heursc does o apply o asymmerc arbues because hey do o decrease he cofdece for he occurrece of a suao. To address he rade-off bewee complee ad paral coame, he fourh reasog echque preses each aspec of coame wh a dmeso usg uly weghs (.e q ad q 2 ) ad combes hem owards ferrg he occurrece of a suao. The uly weghs of wo dmesos deerme whch aspec of coame s more mpora (.e. complee or paral). The cocep of paral ad complee coame ad s reasog echque are appled o FI. mlar o he hrd reasog mehod, FI maps values of symmerc coex arbues o he values of fuzzy ses correspodg o symmerc lgusc varables. Resuls of suao reasog usg he echques dscussed earler suggess he degree of cofdece he occurrece of a suao. I FI, f he oupu of a rule evaluao for he hypereso suao yelds a degree of 0.885, we ca sugges ha he level of cofdece he occurrece of hypereso s Ths value ca be compared o a cofdece hreshold bewee 0 ad (.e. predefed by he applcaos desgers) o deerme wheher a suao s occurrg. The ex seco dscusses he compoe of he AE (Adapao Ege). 5. Adapao Ege (AE) The AE (Adapao Ege) s resposble for gradual ug of daa sream processg parameers accordg o he occurrg suao/s ad avalable resources real me. Lghwegh daa sream mg echques such LWC, LWCLass, RA-Cluser, ERA- Cluser, ad DRA-Cluser [2-3, 20-22] are adapve o avalably of resources va adusg he algorhm parameers. These parameers corol oupu, pu ad/or he process of he algorhm. I hese algorhms, he adapao process s doe hrough Algorhm Graulary (AG) approach. AG has hree dffere varaos of AOG (Algorhm Oupu Graulary), AIG (Algorhm Ipu Graulary) ad APG (Algorhm Processg Graulary) [2-22]. AOG corols he algorhm oupu rae based o he avalably of memory va chagg he daa sream mg algorhm parameers o ecourage or dscourage he creao of ew oupu srucures. mlarly AIG ad APG [22] corol he pu rae ad cosumpo of processg power accordg o he baery level ad CPU usage respecvely. We have spred by he coceps of AG ad developed hree dffere adapao sraeges. These sraeges clude resource-aware, suao-aware ad hybrd sraeges as show Fgure 2. Fgure 2. Adapao of daa sream mg AE cosaly moors occurrg suaos ha are ferred by FI ad avalably of resources repored by RM. Each pre-defed suao eeds o be assged a crcaly value (.e. a value bewee 0 ad ) ha dcaes her mporace. For boh suaos () ad compuaoal resources (R), here are wo hresholds (.e. lower ad upper bouds), a value bewee 0 ad, whch dcae safe, medum ad crcal levels. The hgher he value s, he hgher he suao mporace ad resource usage s. Based o hese levels of crcaly for suaos ad resources, here ca be e possble varaos (cases) of adapao a ru 5

6 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces me. Coroller ha s a subcompoe of AE makes decsos o whch sraegy eeds o be performed accordg o hese hresholds. These e cases are preseed Table 2. We have allocaed he adapao sraeges accordg o hese e cases. Whe resources are crcal meas ha he moble devce ca o coue he mg operaos ad he adapao sraeges ha we provde are o adequae o address he ssue. Therefore oher sraeges such as mgrao of he daa or he process eed o be performed whch are ou of he scope of hs proec. Cases Table 2. Adapao Cases Adapao sraegy - f R a safe level ad a safe level 2- f R a safe level ad a medum level 3- f R a safe level ad a crcal level 4- f R a medum level ad a safe level 5- f R a medum level ad a medum level 6- f R a medum level ad a crcal level 7- f R a crcal level ad a safe level 8- f R a crcal level ad a medum level 9- f R a crcal level ad a crcal level uao-aware sraegy uao-aware sraegy uao-aware sraegy Resource-aware sraegy Hybrd sraegy Hybrd sraegy Oher sraeges e.g. mgrao 5.. Resource-aware Adapao raegy Resource-aware adapao sraegy occurs whe he suao s a safe level bu resource avalably s a medum level. Ths s because ormal suaos do o requre freque moorg ad he resuls of resource-aware adapao do o coradc he requremes of ormal suaos. Resource-awareess s spred by he AG approach. Oe of he AOG-based cluserg algorhms s called LghWegh Cluserg (LWC) [29]. LWC cosders a hreshold dsace measure for cluserg of daa. Icreasg hs hreshold dscourages formg of ew clusers ad ur reduces resource cosumpo. AOG s a hree-sage, resource-aware dsacebased mg daa sreams approach. The process of mg daa sreams usg AOG sars wh a mg phase. I hs sep, a value of hreshold dsace measure s deermed. Ths hreshold has he ably o corol he oupu rae of he rug mg algorhm. The secod sage AOG-mg approach s he adapao phase. I hs phase, he hreshold value s adused o cope wh he daa rae of he comg sream, avalable memory, ad me cosras o fll he memory wh geeraed kowledge (daa mg oupu). The las sage AOG approach s he kowledge egrao phase. Ths sage represes he mergg of geeraed resuls whe he memory s full. Ths egrao allows he couy of he mg process o resource-cosraed devces. The ex subseco dscusses suao-aware adapao sraegy based o he resuls of he FI uao-aware Adapao raegy uao-aware adapao AE s performed whe resources are avalable ad a safe level. uao-aware adapao occurs based o occurrg suaos ferred by FI. These resuls are mulple suaos wh dffere level of cofdece. To provde a fe-graed adapao ad reflecg he level of cofdece of each suao he adapao phase, we compue weghed average of he daa mg parameer value based o cofdece values of suaos ad he pre-se value of he parameer for each suao. The pre-se values of parameers are auomacally calculaed based o he mporace values of he suaos ha wll be dscussed furher he evaluao seco. The suao-aware adapao eables reflecg all he resuls of suao ferece he adapao of parameer values ad s represeed as follows: 3) p = μ p / = = μ where p represes he se value of a parameer for a pre-defed suao, μ deoes he membershp degree of suao where ad represes he umber of pre-defed suaos, ad p represes aggregaed value of he parameer. uao-aware adapao self resuls coseffcecy because whe a suao has a lower mporace value, he compued se value for he hreshold wll be a hgher value. Ths decreases he oupu of he LWC algorhm ad reduces he memory cosumpo. The ex subseco descrbes hybrd adapao sraegy Hybrd Adapao raegy Whe resources are a medum level ad suaos are a medum or crcal level (.e. cases 5 ad 6 Table 2), he coroller apples he hybrd adapao 6

7 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces sraegy. Whe he adapao cases 5 or 6 occurs, resource-aware ad suao-aware adapao sraeges each compue dffere values accordg o resource avalably ad occurrg suaos respecvely. Therefore here s a rade-off bewee he resuls of hese wo sraeges. Hybrd adapao sraegy addresses hs ssue by compug he average value of parameer based o he resuls of he wo sraeges ad crcaly values of he suao ad resource as follows: 4) p I ( p R. crcaly R ) + ( p. crcaly ) = crcaly + crcaly R Havg dscussed he heorecal framework of our work, he followg seco preses he mplemeao ad evaluaos we have performed. 6. Implemeao We have mplemeed a prooype of healh moorg applcao based o FI J2ME ad deployed o a Noka N95 (show Fgure 3). The prooype reasos abou suaos of ormal, prehypoeso, hypoeso, pre-hypereso ad hypereso. Ths applcao ca be used by paes who suffer from blood pressure flucuaos. A rapezodal membershp fuco s used o compue membershp degree of coex values. Coexual formao used cludes sysolc ad dasolc blood pressure (BP ad DBP) ad hear rae (HR). Fgure 3. The prooype of AAP-based healh moorg applcao wh a ECG bosesor To capure he paes hear rae, we have used a wo lead ECG bosesor from Alve Techologes [23] ha rasms ECG sgals usg Blueooh o he moble phoe. For he blood pressure, we have used radomly geeraed daa ha smulaes blood pressure flucuaos. The healh moorg applcao performs suao reasog ad suao-aware adapao real-me o he moble devce usg he LWC algorhm. aus bars o he moble phoe dsplays he level of ceray ad cofdece he occurrece of each suao. The evaluao of FI ad adapao ege s preseed he ex seco. 7. Evaluao For evaluao of AAP, we have performed wo evaluaos. Frs evaluao s a comparave evaluao of FI, C ad Dempser-hafer ad secod evaluao focuses o he adapao of hreshold parameer of LWC accordg o occurrg suaos. 7.. Evaluao of FI To evaluae he FI model, we have compared he FI suao reasog echque o he C ad Dempser-hafer (hereafer D) reasog approaches. The purpose of hs evaluao s frs o valdae he FI model agas a well-kow reasog echque such as D ad a coex model developed for pervasve compug evromes such as C. The secod obecve of he evaluao s o hghlgh he beefs of he FI o deal wh ucera suaos. I hs evaluao, we have cosdered suaos of hypoeso, ormal ad hypereso. These suaos are defed usg coex arbues of sysolc blood pressure (BP) ad dasolc blood pressure (DBP) wh he scale of ad mm Hg ad hear rae (HR) wh he rage of bpm. Table 3 depcs modelg of he hree suaos he C model cludg he weghs of arbues ad her correspodg regos of values. Assged weghs are 0.4 for BP ad DBP ad 0.2 for HR. Table 3. uao defos C uao Coex arbue Rego of values Hypoeso =BP 2=DBP 3=HR Normal Hypereso =BP 2=DBP 3=HR =BP 2=DBP 3=HR >85 ad 35 >60 ad 0 >45 ad 85 >35 >0 >85 The modelg of he hree suaos he FI model s preseed Table 4. Weghs of codos for he FI rules coform o he weghs used C. 7

8 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces Table 4. uao defos FI uao Lgusc Varable Terms represeed below va FI rules =BP 2=DBP 3=HR low, ormal, hgh low, ormal, hgh slow, ormal, fas Rule: f BP s low ad DBP s low ad HR s low he suao s hypoeso Rule2: f BP s ormal ad DBP s ormal ad HR s ormal he suao s ormal Rule3: f BP s hgh ad DBP s hgh ad HR s hgh he suao s hypereso The daase used for he evaluao cosss of 3 coex saes ad her scales corbue o he occurrece of each pre-defed suao as well as he ucera suaos ha occurs whe suaos evolve. Fgure 4 preses he resuls of he evaluao of C, D ad FI for suao reasog abou hypoeso, ormal ad hypereso..2 To apply he D algorhm for reasog abou suaos, we use he Dempser s rule of combao. The ormalzed verso of he combao rule s as follows F_Hypo C_Hypo D_Hypo 5) m( R) = P Q= R P Q= φ m ( P). m ( Q) m ( P). m ( Q) where m(r) deoes he mass value compued for a proposo R gve he evdeces ad. If R represes a suao, cosderg all exsg proposos, he erseco of some of hese proposos deoed as P ad Q resuls he proposo R (.e. ) ad he erseco of oher combaos of proposos resuls a empy se. To model he hree suaos of Hypoeso (L), Normal (N) ad Hypereso (H) wh D, we frs eed o defe proposos ad eves. ce all hree suaos are compable we clude a proposo of Ukow (U) ha would coss of hree suaos. The we defy he eves ad mass values ha reflec he assocao of a eve wh he occurreces of each proposo as depced Table 5. Table 5. Defos of eves ad mass values Eve N L H U BPLow (40-85) BPMed(86-35) BPHgh( ) DBPLow(20-60) DBPMed(6-0) DBPHgh( ) HRlow(20-45) HRMed(46-85) HRFas(86-30) Mass values are assged a way ha hey reflec o wha degree each eve dcaes a suao. ce we have based our suaos o hree coex arbues, we defe hree mass fucos correspodg. The we apply D combao over all proposos ad evdece FI_N C_N D_N F_Hyper C_Hyper D_Hyper Fgure 4. Resuls of he evaluao Fgure 4 shows hree approaches of C, D ad FI have a relavely smlar red accordg o coex chages. Whe he daa correspods o a pre-defed suao he resuls of hree approaches almos overlap. However, whe chages of daa dcae he occurrece of a ukow ad ucera suao, dffereces of reasog resuls bewee C, D ad FI are more appare. Compared o FI, he resuls of suao reasog by he C ad D mehods show sudde rses ad falls wh sharp edges whe suaos chage whch do o mach he real-lfe suaos. Ths s because he D ad C approaches do o deal wh dela chages of 8

9 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces he values ad are o able o reflec he gradual evoluo of oe suao o aoher suao. Whe he value of coex arbues decreases or creases, s membershp degree also creases ad decreases accordgly ad gradually. Ths eables FI o provde more accurae suao reasog resuls erms of reflecg very mor chages of coex. The evaluao valdaes he accuracy of he FI model for suao modelg ad reasog ad also shows ha FI s able o reflec very mor chages of coex suao ferece ad represe chages a more gradual ad smooh maer. The evaluao shows ha he FI model s more approprae approach for represeao of huma coceps ad for reasog abou he real-world suaos ha are defed by couous values. Healh-relaed suaos are examples of hese ypes of scearos where FI ca prove o be more fg approach compared o he D ad C reasog approaches Evaluao of uao-aware Adapao I he mplemeao of he AAP we have used he LghWegh Cluserg (LWC) [29] algorhm as he daa sream mg algorhm. Ths algorhm s oe-pass ad operaes usg he AOG prcpals as dscussed earler he paper. The LWC algorhm provdes adapably by adusg he parameer of hreshold dsace measure accordg o he avalable memory o a devce such as a PDA. I he evaluao of suao-aware adapao, we have adused he parameer of hreshold of LWC accordg o he cofdece level of he occurrg suaos. The values of LWC hreshold for each suao are compued based o he mporace value of each suao ad he mmum ad maxmum values of he hreshold (.e. 6 ad 45 respecvely) usg he followg formula: hreshold=mvalue+(maxvalue-mvalue)*(-mporace) Usg he above formula, f we assg he suaos of ormal, hypereso ad hypoeso he mporace values of 0., 0.9 ad 0.5, he compued hreshold values of each suao wll be 42, 0 ad 26 respecvely. These values are accepable gve a varao of 2 (.e. 42 dvded by 3) for ay of he coex arbues of BP, DBP ad HR has o sgfca mpac o a healhy dvdual whle a varao of 3 for hypereso ca be sgfca. To evaluae he suao-aware adapao, we have used he same 3 coex saes used for he frs evaluao. Fgure 5 shows ha he hreshold value s adused accordg o he cofdece value of each suao. Decreasg he hreshold value creases he umber of he oupu (clusers) ha s requred for closer moorg of more crcal suaos. Level of Cofdece of uao Daa ream Algorhm Threshold FI_N F_Hypo F_Hyper Fgure 5. uao-aware Adapao Resuls The ex seco cocludes he paper ad dscusses fuure work. 8. Cocluso ad Fuure Work I hs paper we proposed ad valdaed a geeral approach for suao-aware adapve processg (AAP) of daa sreams ha corporaes suaoawareess o daa sream processg usg fuzzy logc. The fuzzy suao ferece model allows modelg ad reasog abou real-world ad healhrelaed suaos. The AAP archecure eables realme aalyss of daa emaag from mulple sesors cludg bo-sesors oboard moble devces whle facorg coexual/suaoal formao ad resource avalably. Ths approach sgfcaly ehaces a rage of moble healhcare applcaos. There are several drecos whch we are exedg hs work. We are currely falzg mplemeao ad evaluao of hybrd adapao usg RA-Cluser [22] ha eables adapao of he parameers of radus hreshold, radomzao facor ad samplg rae accordg o he memory, CPU ad baery usage respecvely. Furhermore, we are workg o exesve esg of our prooype realworld suao couco wh releva healhcare professoals ad doma expers order o develop a udersadg of hgh rsk suaos for he moorg of paes ad defyg wha formao s requred from bo-sesors. 0. Refereces [] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Ubquous Daa ream Mg, Curre Research ad Fuure Drecos Workshop Proceedgs held couco 9

10 Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces wh The Eghh Pacfc-Asa Coferece o Kowledge Dscovery ad Daa Mg, ydey, Ausrala, [2] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Resource-Aware Mg of Daa reams, Joural of Uversal Compuer cece. (8), 2005, pp [3] M.M. Gaber, A. Zaslavsky ad. Krshaswamy, A Cos-Effce Model for Ubquous Daa ream Mg, Proceedgs of he Teh Ieraoal Coferece o Iformao Processg ad Maageme of Uceray Kowledge-Based ysems, Peruga Ialy, [4] A. Padovz,. Loke, ad A. Zaslavsky, Towards a Theory of Coex paces, I Proceedgs of he 2d IEEE Aual Coferece o Pervasve Compug ad Commucaos, Workshop o Coex Modelg ad Reasog ( CoMoRea ), Orlado, Florda: IEEE Compuer ocey, [5] D. Kosaas, V. Joes, ad R. Buls, MobHealh- Iovave 2.5/3G Moble ervces ad Applcaos for Healhcare, 2007 [6] P. Rubel, J. Fay, G. Nollo, D. Assaell, B. L, L. Reser,. Adam,. Arod, H. Aou, M. Ohlsso, L mo- Chauemps, D. Te lsso, C. Maloss, G. Zla, A. Galass, L. Edebrad, ad P. Chevaler, Toward Persoal ehealh Cardology: Resuls from he EPI-MEDIC Telemedce Proec, Joural of Elecrocardology 2005, 38: pp [7] K. Laerhove, L. Bey, J. Ng J.,. Themarus, R. Kg,. Kwa, H. Gellerse, M. loma, O. Wells, P. Needham, N. Peers, A. Darz, C. Toumazou, G. Yag, "Medcal healhcare moorg wh wearable ad mplaable sesors", UbHealh 2004: The 2d Ieraoal Workshop o Ubquous Compug for Pervasve Healhcare Applcaosm, [8] P. Ledekkers, ad V. Gay, Persoal Hear Moorg ad Rehablao ysem usg mar Phoes, I Proceedgs of he Ieraoal Coferece o Moble Busess (ICMB'05), [9] P. Varkar,. gh, Y. Lee, Coex-Aware Daa Mg Framework for Wreless Medcal Applcao, Lecure Noes Compuer cece (LNC), Volume 2736, prger-verlag. IBN , 2003, pp [0] B. Jase, ad R. Deklerek, Coex-Aware Iacvy Recogo for Vsual Fall Deeco, I Pervasve Healh Coferece ad Workshops, [] Km, H., ad Jo, H. A Coex-Aware Traveler Healhcare ervce (TH) ysem, I Pervasve Healh Coferece ad Workshops. November-December [2] M. Becker, E. Werkma, M. Aasasopoulos, ad T. Kleberger, Approachg Ambe Iellge Home Care ysems, I Pervasve Healh Coferece ad Workshops, [3] H. Me, I. Wdya, A. Halere ad B. Erfao, A Flexble Val g Represeao Framework for Moble Healhcare, [4] G. Brelecker, H. chuld, ad R. chaz, Hyperdaabases for Peer-o-Peer Daa ream Processg, I Proceedgs of he IEEE Ieraoal Coferece o Web ervces (ICW'04). a Dego, Calfora, [5] C. Che, H. Agrawal, M. Cochwala ad D. Rosebluh, ream query processg for healhcare bosesor applcaos, I Proceedgs of he 20h Ieraoal Coferece o Daa Egeerg (ICDE'04). Boso, MA, UA: IEEE Compuer ocey, [6] A. Padovz, A. Zaslavsky ad. Loke, A Ufyg Model for Represeg ad Reasog Abou Coex uder Uceray, h Ieraoal Coferece o Iformao Processg ad Maageme of Uceray Kowledge- Based ysems (IPMU), Pars, Frace), [7] Jag, J.R., u, C. ad Mzua E., Neuro-Fuzzy ad of Compug: A Compuaoal Approach o Learg ad Mache Iellgece. Prece-Hall: Upper addle Rver, NJ, 997. [8] Bruce, G. Buchaa B.G. ad horlffe, E.D., Rulebased exper sysems : he MYCIN expermes of he aford Heursc Programmg Proec. Readg, Mass: Addso-Wesley, 984. [9] Zmmerma, H., Fuzzy e Theory - ad Is Applcaos. Kluwer Academc Publshers, Norwell, Massachuses, 996. [20] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Oboard Mg of Daa reams esor Neworks, A Book Chaper Advaced Mehods of Kowledge Dscovery from Complex Daa, (Eds.). Badhyopadhyay, U. Maulk, L. Holder ad D. Cook, prger Verlag. [2] M.M. Gaber,. Krshaswamy, ad A. Zaslavsky, Adapve Mg Techques for Daa reams Usg Algorhm Oupu Graulary, The Ausralasa Daa Mg Workshop (AusDM 2003), Held couco wh he 2003 Cogress o Evoluoary Compuao (CEC 2003), Caberra, Ausrala, prger Verlag, Lecure Noes Compuer cece (LNC), [22] N. Phug, M.M. Gaber ad U. Roehm, Resource-aware Dsrbued Ole Daa Mg for Wreless esor Neworks, Proceedgs of he Ieraoal Workshop o Kowledge Dscovery from Ubquous Daa reams (IWKDUD07), couco wh ECML ad PKDD 2007, Warsaw, Polad, [23] Alve Techologes, hp:// 0