THE REINFORCEMENT FRAMEWORK OF A DECISION SUPPORT SYSTEM FOR THE LOCALIZATION AND MONITORING OF INTELLIGENT REMOTE BIO ROBOTS

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1 The 0 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPTATION and COMMUNICATION - 00 Proceedings of he 0h Inernaional Conference Reliabiliy and Saisics in Transporaion and Communicaion (RelSa 0), 0 Ocober 00, Riga, Lavia, p ISBN Transpor and Telecommunicaion Insiue, Lomonosova, LV-09, Riga, Lavia THE REINFCEMENT FRAMEWK OF A DECISION SUPPT SYSTEM F THE LOCALIZATION MONITING OF INTELLIGENT REMOTE BIO ROBOTS Dale Dzemydiene, Ananas Andrius Bielskis, Arunas Andziulis,, Darius Drungilas,, Ramunas Dzindzaliea,, Gediminas Gricius Mykolas Romeris Universiy Aeiies sr. 0, LT- 080 Vilnius, Lihuania daledz@mruni.eu Klaipėda Universiy Mano 84, 994 Klaipėda, Lihuania andrius.bielskis@ik.ku.l, arunas@ik.ku.l, Insiue of Mahemaics and Informaics Akademijos sr. 4, Vilnius, Lihuania doriion@gmail.com, ramunas.dzindzaliea@eo.l This paper analyses he possibiliies of he inegraion of differen echnological and knowledge represenaion echniques for he developmen of reinforcemen frameworks for he remoe conrol of muliple agens such as wheelchair-ype robos. Some echnological soluions are discussed regarding he recogniion of localizaion of moving objecs by using mobile echnologies. Large-scale muli-dimensional recogniions of emoional diagnoses of disabled persons ofen generae large amouns of mulidimensional daa wih complex recogniion mechanisms, based on he inegraion of differen knowledge represenaion echniques and complex inference models. The problem is o reveal he main componens of a diagnosis as well as o consruc flexible decision making models. Sensors can help o record primary daa for monioring objecs; however he recogniion of abnormal siuaions, he clusering of emoional sages and resoluions for cerain ypes of diagnoses is an oncoming issue for bio-robo consrucors. The predicion crieria of he diagnosis of he emoional siuaion of disabled persons are described using knowledge based models of neural neworks. The research resuls presen he developmen of a muli-layered framework archiecure wih he inegraion of arificial agens and suppor componens for diagnosis recogniion and conrol, or furher acions, by using mobile echnologies. The mehod of fuzzy neural nework conrol of he speed of wheelchair-ype robos working in real ime by providing movemen suppor for disabled individuals is presened. The fuzzy reasoning by using fuzzy logical Peri nes is described in order o define he physiological sae of disabled individuals hrough recognizing heir emoions during heir differen aciviies. Some new possibiliies of he recogniion of moving objec locaion are inroduced in he sysem. Keywords: muliple agen sysem, decision suppor sysem, knowledge represenaion echniques, fuzzy logic, neural neworks, Peri nes. Inroducion The developmen process of inelligen sysems wih adapive e-services is imporan for providing user-friendly e-healh and e-social care for people wih movemen disabiliies. Such sysems include differen inellecual componens for conrol and for monioring sensors by supporing muli-agen aciviies. In addiion, in accordance wih he recogniion of cerain siuaions, hese sysems inegrae he possibiliies o affec and conrol he devices used by disabled persons [4, 6, 5]. We recognize he possibiliies of developing he inegraion of differen ypes of knowledge represenaion echniques in such bio-robo sysems wih working on-line sub-sysems of complex mechanisms of cooperaion of muli-agen aciviies for sensing human affec. The framework provides inelligen acciden prevenive robo-based suppor for people wih movemen disabiliies and includes affec sensing in Human Compuer Ineracion (HCI) in providing e- healh care for people wih movemen disabiliies, Human-Robo Ineracion (HRI) for assising elehealhcare paiens o remain auonomous, and Compuer Mediaed Communicaion (CMC), o provide adapive user-robo friendly collaboraion. Such sysems should depend upon he possibiliy of exracing emoions wihou inerruping he user during HCI, HRI, or CMC [,, 4, 6, 8, 9]. Emoion is a mindbody phenomenon accessible a differen levels of observaion (social, psychological, cerebral and physiological). The coninuous physiological aciviy of a disabled person is being made accessible by use of inelligen agen-based bio-sensors coupled wih compuers. The aim of his research concerns invesigaions ino he inegraion of differen knowledge represenaion echniques in order o develop a reinforcemen framework of muliple cooperaive agens 07

2 Session. Inelligen Transpor Sysems aciviies in order o recognise he predicion crieria of he diagnoses of he emoional siuaions of disabled persons. The research resuls presen furher developmen of a muli-layered model of his framework, wih inegraion of he evaluaion of localizaion possibiliies and decision suppor sysem consrucions. The knowledge of decision suppor sysems is represened by fuzzy neural conrol of he speed of wo wheelchair-ype robos working in real ime providing movemen suppor for disabled individuals. The mehod of fuzzy reasoning using fuzzy logical Peri nes [5] is described in order o define he physiological sae of disabled individuals by recogniion of heir emoions.. The Framework Archiecure of he Adapive Conrol of a Muli-agen Sysem Working wih Robo Moion Recogniion Componens The proposed reinforcemen framework is based on he ineracion of inelligen remoe bio-robos, localizaion services, embedded decision suppor sysems and daa sored in a daa warehouse (Fig. ). The daa warehouse is based on disribued informaion sysems wih imporan personal daa of he paiens and sensor monioring daa. The framework includes he adapive moving wheelchair-ype robo which is remoely communicaing wih a wearable human affec sensing bio-robo. To record, for reasons of e-healh care, relevan episodes based on humans affec sages [], he conex aware sensors are incorporaed ino he design of he Human Affec Sensing Bio Robo-x (HASBR-x) for every disabled individual, and ino he local Inelligen Decision Making Agen-x (IDMA-x) for every inelligen suppor providing robo. Figure. The reinforcemen framework of an inelligen remoe bio robo ineracion based on disribued informaion sysems This framework allows a muli-sensor daa fusion before he ransmission of he daa o he Remoe Conrol Server (RCS) o minimize he TCP/IP (UDP) bandwidh usage. Muli-agen based adapive moion conrol of boh robos is based on an adapive Fuzzy Neural Nework Conrol (FNNC) approach. The archiecure of he FNNC conroller represens an approach of Adapive Neural Fuzzy Inference Sysem (ANFIS) ha combines he fields of fuzzy logic and neural neworks [] (Fig. ). NN Learning Agen Learning Algorihm Robo conrol agen v(k) + Σ - -Z - v 0 (k) e(k) Δe(k) ARTIFIC IAL NN ATMEGA ΔPW(k) + Σ Z - + Moor Speed PW(k) Figure. Modified agen based adapive FNNC-ype DC moor speed conroller according o [] 08

3 The 0 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPTATION and COMMUNICATION - 00 The abiliy o learn abou he nonlinear dynamics and exernal disurbances of he moor speed conroller wih a sable oupu, small seady error, and fas disurbance rejecion is inegraed ino his framework. A he k h momen, he difference beween moor speed reference value v(k) and moor speed oupu value v o (k) is spli o speed error e(k) and speed error change Δe(k). These values are used as proposed in [6] NN Learning Agen in Fig. for learning he arificial neural nework Arificial NN in Fig. as well as he nd order inpu vecor of he Arificial NN. The oupu of he Arificial NN generaes a percenage value of pulse widh change ΔPW(k) o describe how much pulse widh value PW(k) of he real moor speed conrol value a he momen k should be changed. This value hen is generaed in real ime by he ATmega microconroller o perform online calculaing: PW ( k ) PW ( k ) + ΔPW ( k ) =. () NN Weigh DB NN Learning Agen RS Inerface NN Learning DB Microconroller FNNC Figure. Muli-agen based adapive robo moor speed conrol sysem using Agen-based NN learning sysem [6] The archiecure of he neural-fuzzy conroller [] for DC moor speed conrol of a wheelchairype robo is presened in Fig. 4. There layer represens inpus X = e(k) and Y = δe(k) o he fuzzy neural conroller, he speed error e(k) and he change in speed error δe(k) = e(k) e(k-), respecively. Layer consiss of 7 inpu membership nodes wih four membership funcions, A, A, A, and A4, for inpu X and hree membership funcions, B, B, and B, for inpu Y []. Each node in layer acs as a linguisic label of one of he inpu variables and, i.e., he membership value specifying he degree o which an inpu value belongs o a fuzzy se is deermined in his layer. The riangular membership funcion is chosen owing o is simpliciy. For he change in moor speed error δe(k), he iniial values of he premise parameers (he corner coordinaes aj, b j and c j of he riangle) are chosen so ha he membership funcions are equally spaced along he operaing range of each inpu variable. The weighs beween inpu and membership level are assumed o be a uniy. The oupu of neuron j =,,, and 4 for inpu i = and j =,, and for inpu i = in he second layer can be obained as follows. For posiive riangle slope if X i a j and X i b j O j = (X i a j )/(b j a j ), Or for riangle negaive slope if X i b j and X i c j O j = (X i c j )/(b j c j ), Where a j, b j, and c j are he corners of he j h riangle ype membership funcion in layer and X i is he i h inpu variable o he node of layer, which could be eiher he value of he error or he change in error. The layer in Fig. represens inpus X = e (k) and Y = δe (k} o he fuzzy neural conroller, he speed error e(k) and he change in speed error δe(k) = e(k) e(k-), respecively. Layer consiss of 7 inpu membership nodes wih four membership funcions, A, A, A, and A4, for inpu X and hree membership funcions, B, B, and B, for inpu Y. The weighs beween he inpu and membership levels are assumed o be a uniy. Each node in Rule layer muliplies he incoming signal and oupus he resul of he produc represening one fuzzy conrol rule. I akes wo 09

4 Session. Inelligen Transpor Sysems inpus, one from nodes A A4 and he oher from nodes B B of layer. Nodes A A4 define he membership values for he moor speed error and nodes B B define he membership values for he change in speed error. Accordingly, here are nodes in layer o form a fuzzy rule base for wo inpu variables, wih four linguisic variables for he inpu moor speed error e(k) and hree linguisic variables for he inpu change in moor speed change error δe(k). The inpu/oupu links of layer define he precondiions and he oucome of he rule nodes, respecively. The oucome is he srengh applied o he evaluaion of he effec defined for each paricular rule. The oupu of neuron k in layer is obained as O k = W jk *y j, where y j represens he j h inpu o he node of layer and W jk is assumed o be a uniy. Neurons in he oupu membership layer 4 represen fuzzy ses used in he consequen fuzzy rules. An oupu membership neuron receives inpus from corresponding fuzzy rule neurons and combines hem by using fuzzy operaion union. This was implemened by he maximum funcion. Layer 4 acs upon he oupu of layer muliplied by he connecing weighs. These link weighs represen he oupu acion of he rule nodes evaluaed by layer, and he oupu is given O 4m = max (O k *W km ), where he coun of k depends on he links from layer o he paricular m h oupu in layer 4 and he link weigh W km is he oupu acion of he m h oupu associaed wih he k h rule. This level is essenial in ensuring he sysem s sabiliy and allowing smooh conrol acions. Layer 5 is he oupu layer and acs as a defuzzifier. The single node in his layer akes he oupu fuzzy ses clipped by he respecive inegraed firing srenghs and combines hem ino a single fuzzy se. The oupu of he neuron-fuzzy sysem is crisp, and hus a combined oupu fuzzy se mus be defuzzified. The sum-produc composiion mehod was used. I calculaes he crisp oupu as he weighed average of he cancroids of all oupu membership funcions as O 5o = Sum (O 4m *a cm *b cm ) / Sum (O 4m *b cm ), where a Cm and b Cm for m =,,.., and 5 are he cenres and widhs of he oupu fuzzy ses, respecively. The values for he b Cm s were chosen o be a uniy. This scaled oupu corresponds o he conrol signal (percen duy cycle) o be applied in order o mainain he moor speed a a consan value. The only weighs ha are rained are hose beween layer and layer 4 of Fig.. A back-propagaion nework is used o rain he weighs of his layer. The weighs of he neural nework were rained offline by using an open source ype R-programming environmen before hey were used in he online real ime experimen by applying he modified learning algorihm from []: Sep (): Calculae he error for he change in he conrol signal (duy cycle) for ATmega-based microconroller as E o = T o O 5, where E o, T o, and O 5 are he oupu error, he arge conrol signal, and he acual conrol signal; Sep (): Calculae he error gradien δ m = (T o O 5o )*(Sum (O 4j (a cm - a cj ) for j = o m and j <> m) / Sum (O 4j for j = o m)*, where a Ci for i = 5 are he cenres of he oupu fuzzy ses and O 4j is he firing srengh from node j in layer 4; Figure 4. Archiecure of he neural-fuzzy conroller by [] for DC moor speed conrol of wheelchair ype robo 0

5 The 0 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPTATION and COMMUNICATION - 00 Sep (): Calculae he weigh correcion Δ wkm = ηδ m O k o increase he learning rae. Here a Sejnowski Rosenberg updaing mechanism was used, which akes ino accoun he effec of pas weigh and changes in he curren direcion of he movemen in he weigh space. This is given by Δ wkm () = η( α)δ m O m + αδ wkm ( ), where α is a smoohing coefficien in he range of 0,0, and η is he learning rae; Sep (4): Updae he weighs w km ( + ) = w km () + Δw km (), where is he ieraion number. The weighs linking he rule layer (layer ) and he oupu membership layer (layer 4) are rained o capure he sysem dynamics and herefore minimize he ripples around he operaing poin.. Localizaion Possibiliies of Moving Objecs In order o ideniy he locaion of a moving objec, we will use he package javax.microediion.locaion in JME s locaion (JSR 79) [8]. Locaion service: LDS answers basic quesions: where he objec is (e.g. coordinaes), and how you can ge o he objec. The locaion deecion service enables us o obain informaion abou he insecuriy of he localiy, or, if an acciden happens, we will be able o inform he appropriae insiuions abou he inciden. The package javax.microediion.locaion enables us o wrie applicaion programs (APP) o idenify he locaion o be equipped wih limied resources [, 4]. JME programming inerface specificaion can be implemened dynamically by any of our lised local mehods. The main JME programming inerface provides he curren physical locaion wih mobile devices. Hardware equipmen for he plaform deermines which mehods are suiable for a locaion and which of hem i suppors. APP may require cerain properies from he supplier, such as a degree wih he minimum precision. APP should warn he user before he sars using one of he mehods. JSR 79 requiremen: "Conneced Device Configuraion (CDC) or Conneced Limied Device Configuraion (CLDC) wih. versions. The Conneced Limied Device Configuraion (CLDC) is no suppored wih version.0, because here are no floaing-poin numbers, which are used in APP o show he coordinaes and oher dimensions [8]. The package javax.microediion.locaion is designed as he main class in which oher classes are included: Crieria class (cr) wih he parameers of accuracy, response ime, heigh and speed; if we specify he sar of recogniion of he objec, hen we creae a new crieria class: Crieria cr = new Crieria (); and define he horizonal accuracy: cr.sehorizonalaccuracy(500) (in his case 500 meers). Locaion class exracs he local resuls. Is objec conains coordinaes, speed if reached, and he address where he ex is reached and he ime marker, which shows he dimensions of he space. The coordinaes are designed for wo classes: Coordinaes of he objec represen poins of laiude and longiude in degrees, and he heigh in meers. Qualified Coordinaes of he objec conain laiude, longiude, and also heir accuracy, which are depiced as he radius of he area. Examples of his specificaion are presened as follows: Crieria cr = new Crieria (); cr.sehorizonalaccuracy (500); LocaionProvider locpro = LocaionProvider.geInsance (cri); Locaion loc = locpro.gelocaion (60); Coordinaes loc.gequalifiedcoordinaes cor = (); if (cor! = null) ( double la = cor.gelaiude (); double lon = cor.gelongiude (); The session iniiaion proocol (SIP) is a signalling proocol for applicaions ha creae, modify and complee sessions beween one or more paricipans []. SIP cliens use TCP or UDP pors (usually por 5060) o connec o he SIP server or o oher erminal sysems. SIP is programmed o be applied o JME. Some supplemens are needed in order o adap he session iniiaion proocol (SIP) echnology o mobile devices [9, 0]. The supplemened javax.microediion.connecion package is creaed as a specific javax.microediion.sip package, which provides he connecion sessions beween he SIP cliens []. Connecions beween he conceps of open and complee clien and server connecions are made by sending he necessary sreams of daa. I is necessary o view he classes, requiring he use of SIP echnology. The main programming inerface classes of he package are presened and described in Fig. 5. The SIP-based sysem mus be designed according o he requiremens ha enable us o send he required daa: he coordinaes of he locaion, he objec idenificaion, he saus of he objec and oher

6 Session. Inelligen Transpor Sysems relevan parameers. SIP-based locaion informaion is ordered using he SUBSCRIBE message, and i noifies abou saus changes of he objec by means of a NOTIFY message. Differen sources of informaion such as a mobile phone or oher equipmen can give addiional informaion provided o a server abou ime momens if he sensor informaion is used. This funcion is performed by he SIP PUBLISH message funcional ineroperabiliy. The noificaion is a user agen ha generaes NOTIFY requess for he purpose of noifying subscribers abou he sae of a resource. Typically noifiers also accep SUBSCRIBE requess o creae subscripions. Figure 4. Classes of exension package javax.microediion.connecion (according o [8, 9]) Noificaion is he ac of sending a NOTIFY message o a subscriber o inform he subscriber of he sae of a resource. A subscriber is an agen ha receives NOTIFY requess; hese NOTIFY requess conain informaion abou he sae of a resource he subscriber is ineresed in. Typically subscribers also generae SUBSCRIBE requess and hen send hem o noificaion acions o creae subscripions. When a change in he subscribed sae occurs, he noificaion immediaely consrucs and sends a NOTIFY reques o inform subscribers of changes in he sae o which he subscriber has a subscripion. Mobile services can provide daa abou he changing posiion of a user s erminal in geographical dimensions. Mobile Web services may be added o differen erminals and a relaionship will be possible if he inerface is he same. Such a realizaion is inappropriae in our case because i will no have he possibiliy of building up sessions beween moving erminals. We have o use he mobile Web services beween he erminals as PP ha can use SIP sessions. The end poins of mobile Web services are SIP URI. Web service end-poins are wo poins of he URI which consis of he IP addresses. Figure 5. Common scheme of he relaionship beween Preseniy and Wacher. Human Compuer Ineracion in he Sysem There are many differen mehods of recognizing physical sae or behaviour by using daa from a wearer's emoion recogniion sensors [5-9]. A modified Arousal Valence model from [6] was used o discover informaion in real ime in order o provide some friendly advice o a person wih movemen disabiliies. The framework presened in Figure uses four emoion recogniion sensors for each disabled individual: ECG (Elecrocardiogram), SCR (skin conducance response), STH (skin emperaure of head), and STF (skin emperaure of finger) o provide HR(hear rae), HRVH(hear rae variabiliy for he range of 0.5 o 0.4 Hz), HRVL (hear rae variabiliy for he range of 0.05 o 0.5 Hz), SCR, STH, and STF inpus for defining fuzzy values of arousal and valence (Fig. 4).

7 The 0 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPTATION and COMMUNICATION - 00 The sysem uses 67 rules o ransform he inpus (he arousal and he valence) ino he 5 oupus (fun, challenge, boredom, frusraion, and exciemen). The fuzzy sysem model from [6] is used for recogniion of five emoional saes (fun, challenge, boredom, frusraion, and exciemen) from arousal and valence. Figure 6. Fuzzy sysem of [6] for recogniion of five emoional saes (fun, challenge, boredom, frusraion, and exciemen) from arousal and valence The compuing resuls of fuzzy reasoning were obained by he applicaion of fuzzy logic Peri nes []. A classical Peri ne is defined as a srucure N = <S, T, F> where S means se of places, T is se of ransiions and F is F (S x T) (T x S), where ( T)( p, q S)(p, ), (, q) F. Graphical represenaion is se up by he following symbols: places - by rings, ransiions - by recangles, and relaions by poiners beween ransiions and places or places and ransiions. In classical Peri nes, here is a oken placed if he expression is rue () or no if i is false (0). Any IF-THEN rule is given in he form of IF X is Α... X n is Αn THEN Y is Β, where A,.., A n and B are cerain predicaes characerizing he variables X,...,X n and Y. The se of IF- THEN rules forms he linguisic descripion: R : = IF X is Α... X is Α THEN Y is Β R : = IF X is Α m m n n n... X is Α mn THEN Y is Β m where each ransiion of he resul fuzzy Peri ne corresponds o one rule of linguisic descripion. For recogniion of diagnosis, rules are consruced for deermining he galvanic skin response (GSR), hear rae (HR), hear rae variabiliy high (HRV H ), hear rae variabiliy low, (HRV L ), and skin emperaure (Table). Table. Models of Logical Peri Nes Applied o Transforming 89 Fuzzy Inference Rules o Consrucing Suppor Informaion Sysem for Bio Robos No Applied LPN Model Fuzzy Compuing Applied Transiions α = λα if α θ T, T, T 6, T 59, T 60, T 6, T 65, T 84, T 85, T 86, T 87, T 88, T 89 α λ max { α, α } = α θ i= α λ min { α, α } = T, T 4, T 0, T, T T 5-8, T -5, T 7-50, T 6-64, T 66-8 α θ i= 4 α = λ min { α, α α } 4 α θ, i= T 9

8 Session. Inelligen Transpor Sysems Coninuaion of Tabl. No Applied LPN Model Fuzzy Compuing Applied Transiions 5 α = λ α if α θ T 5-5, T α = λ 4 α = λ α if α θ 4 α = λ 5 max αi θ i= α = λ α if α θ α = λ α if α θ mmin αi θ i= 4 { α, α } α = λ α if α θ T 5-54 { α, α, α } 4 The se of 67 rules from [6] and corresponding ransiions of logical Peri nes are used in he ransforming of arousal-valence space ino five modelled emoional saes for convering arousal and valence ino boredom, challenge, exciemen, frusraion, and fun. Table, shows some models of logical Peri nes applied o he ransforming of 89 of fuzzy inference rules for consrucing a real ime suppor informaion sysem for bio robos of he model of Fig.. A se of rules and corresponding ransiions is proposed for deermining galvanic skin response (GSR), hear rae (HR), hear rae variabiliy high (HRV H ), hear rae variabiliy low (HRV L ), skin emperaure of head (ST H ), and skin emperaure of finger (ST F) ino arousal and valence. Table. Examples of rules and corresponding ransiions proposed in concering diagnosis from sensor s daa No Rules Transiions If (GSR is high) hen (arousal is high) T If (GSR is high) or (HR is high) hen (arousal is high) T If (GSR is mid-low) hen (arousal is mid-low) T 4 If (GSR is low) or (HR is low) hen (arousal is low) T 4 5 If (GSR is low) and (HR is high) hen (arousal is mid-low) T 5 6 If (GSR is high) and (HR is low) hen (arousal is mid-high) T 6 7 If (GSR is high) and (HR is mid) hen (arousal is high) T 7 8 If (GSR is mid-high) and (HR is mid) hen (arousal is mid-high) T 8 9 If (GSR is mid-low) and (HR is mid) hen (arousal is mid-low) T 9 0 IF (HRV H is high) and (HRV L is low) hen (valence is very-high) T 0 IF (HRV H is low) and (HRV L is high) hen (valence is very-low) T IF (HRV H is medium) and (HRV L is medium) hen (valence is neural) T IF (HRV H is high) and (HRV L is medium) hen (valence is high) T 4 IF (HRV H is medium) and (HRV L is high) hen (valence is low) T 4 5 IF (HRV H is medium) and (HRV L is low) hen (valence is high) T 5 6 IF (HRV H is low) and (HRV L is medium) hen (valence is low) T 6 7 IF (HRV H is high) and (HRV L is high) hen (valence is neural) T 7 8 IF (HRV H is low) and (HRV L is low) hen (valence is neural) T 8 9 IF (HR is high) and (HRV H is high) and (HRV L is high) hen (valence is high) T 9 0 IF (ST H is high) or (ST L is low) hen (valence is low) T 0 IF (ST H is low) or (ST L is high) hen (valence is high T IF (ST H is medium) or (ST L is medium) hen (valence is medium) T 4

9 The 0 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPTATION and COMMUNICATION - 00 The se of 67 rules from [6] and corresponding ransiions of logical Peri nes are proposed for deermining he ransformaion of arousal-valence space ino five modelled emoional saes o conver arousal and valence ino boredom, challenge, exciemen, frusraion, and fun (Table ). Table. Examples of descripion of rules from he se of 67 rules from [6] No Rules Transiions If (arousal is no very low) and (valence is mid-high) hen (fun is low) T 4 If (arousal is no low) and (valence is mid-high) hen (fun is low) T 4 5 If (arousal is no very low) and (valence is high) hen (fun is medium) T 5 6 If (valence is very high) hen (fun is high) T 6 7 If (arousal is mid-high) and (valence is mid-low) hen (challenge is low) T 7 8 If (arousal is mid-high) and (valence is mid-high) hen (challenge is low) T 8 9 If (arousal is high) and (valence is mid-low) hen (challenge is medium) T 9 0 If (arousal is high) and (valence is mid-high) hen (challenge is medium) T 0 If (arousal is very high) and (valence is mid-low) hen (challenge is high) T Such rules are consruced as he schema of ransiions of Logical Peri Nes proposed for deermining he ransformaion of arousal-valence space ino five modelled emoional saes o conver arousal and valence ino boredom, challenge, exciemen, frusraion, and fun. To deermine he emoions of users during heir relaxaion sae, agens HARA-, HARA-, IDMA-, and IDMA-, presened in Fig., were programmed using he following reasoning algorihm of fuzzy logical Peri nes. This algorihm receives a fuzzy Peri ne as an inpu and creaes a se of linguisic descripions corresponding o each oupu place of a fuzzy Peri ne. Human arousal recogniion agens HARA- and HARA- from Fig. were programmed o use hese reasoning algorihms o creae some friendly advice for disabled individuals. 4. Conclusions An approach for developing he ineracion archiecure of mobile devices and remoe server sysems wih addiional funcionaliies for conexual informaion ransmission is proposed. Some mobile 5

10 Session. Inelligen Transpor Sysems soluions are included for recogniion of he locaion of moving objecs in he process of monioring remoe agens. The recogniion of he diagnosis of he emoional siuaion of disabled persons is based on a muli-layered model which inegraes several echniques of knowledge represenaion: neural neworks, fuzzy logic Peri nes, and evaluaion of fuzzy neural conrol of speed of wheelchair ype-robos working in real ime. This was done by implemening movemen suppor for disabled individuals using informaion based on he emoional sae of he disabled persons. The proposed framework uses four emoion recogniion sensors for each disabled individual: he ECG (Elecrocardiogram), he SCR (Skin Conducance Response), he ST H (Skin Temperaure of Head), and he ST F (Skin Temperaure of Finger) o provide HR(Hear Rae), HRV H (Hear Rae Variabiliy for he range of 0.5 o 0.4 Hz), HRV L (Hear Rae Variabiliy for he range of 0.05 o 0.5 Hz), SCR, ST H, and ST F inpus for defining fuzzy values of arousal and valence of disabled person. The mehod of fuzzy reasoning using fuzzy logical Peri nes based on ransforming arousalvalence space ino five modelled emoional saes o conver arousal and valence ino boredom, challenge, exciemen, frusraion, and fun is described. The mehod allows physiological sae of disabled individuals o be defined, and gives hem online advice based on he recogniion of heir emoions during heir aciviies. References. Penland A Healhwear: Medical Technology Becomes Wearable. IEEE Compuer, 7(5): Villon O., Lise, Ch A User-Modeling Approach o Build User s Psycho-Physiological Maps of Emoions using Bio-Sensors. Proc. of IEEE Roman, pp Rubaai, A., Ofoli A.R., Burge, L. and M. Garuba Hardware Implemenaion of an Adapive Nework-Based Fuzzy Conroller for DC DC Converers// IEEE Transacions on Indusry Applicaions, Vol. 4, No. 6: Bielskis, A.A., Denisov, V., Kučinskas, G., Ramašauskas, O., Romas, N Modeling of Human Physiological Parameers in an E-Laboraory by SOM Neural Neworks// Elekronika Elekroechnika. Vol. (75): Papillo J.F., Shapiro, D The cardiovascular sysem. In: Cacioppo, J.T., Tassinary, L.G. (Eds.), Principles of Psychophysiology: Physical, Social, and Inferenial Elemens. Cambridge Universiy Press, Cambridge, England, pp Mandryk, R.L., Akins, M.S A fuzzy physiological approach for coninuously modeling emoion during ineracion wih play echnologies, In. J. Human-Compuer Sudies 65, 007, pp Rowe, D.W., Siber, J., Irwin, D Hear rae variabiliy: Indicaor of user sae as an aid o human-compuer ineracion. Proc. of he Conf. on human facor in compuer science, pp Wilson, G.F. 99. Applied use of cardiac and respiraion measures: Pracical consideraions and precauions// Biological Psychology, 4: Korhonen, I Mehods for he analysis of shor-erm variabiliy of hear rae and blood pressure in frequency domain: PhD hesis, VTT Publicaions. hp:// 0. Touze, C. F Neural Neworks and Q-Learning for Roboics. IJCNN '99 Tuorial, Inernaional Join Conference on Neural Neworks, Washingon, DC.. McCallum, R.A., 995. Insance-based Sae Idenificaion for Reinforcemen Learning, Advances in Neural Informaion Processing Sysems 7, MIT Press, USA.. Sanos, J.M., Touze, C Dynamic Updae of he Reinforcemen Funcion during Learning// Connecion Science, Special issue on Adapive Robos, (C. Torras gues ed.).. Jiang, C., Zheng, Y. Fuzzy Reasoning Based on Peri Nes. hp:// ) 4. Bielskis, A.A., Denisovas, V., Drungilas, D., Gricius, G., Ramašauskas. O Modeliing of Inelligen Muli-Agen based E-healh Care Sysem for People wih Movemen Disabiliies// Elekronika ir elekroechnika. Kaunas: Technologija, 6 (86): Pavliska V Peri Nes as Fuzzy Modeling Tool. hp://irafm.osu.cz/research_repor/84_rep84.pdf ) 6. Dzemydienė D., Maskeliūnas S., Jacobsen K. 008 Susainable managemen of waer resources based on web services and disribued daa warehouses// Technological and Economic Developmen of Economy: Balic journal on susainabiliy.vol. 4, No. :

11 The 0 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPTATION and COMMUNICATION Booh D., Haas H., McCabe F., E. Newcomer, M. Champion, and Orchard C. F. D. 004 Web Service Archiecure. WC Recommendaion. hp:// 0040/ 8. Mahmoud H JME and Locaion-Based Services. 9. Huang, C. M., C. H. Lee, and J. R. Zheng A novel SIP-based roue opimizaion for nework mobiliy. IEEE Journal on Seleced Areas in Communicaions 4(9): Rosenberg J., H. Schulzrinne, G. Camarillo, A. Johnson, J. Peerson, R. Sparks, M. Handley, and Schooler E. 00. SIP: Session Iniiaion proocol, RFC 6. hp:// Johnson, D., C. Perkins, and J. Arkko Mobiliy Suppor in IPv6. IETF RFC Locaion API for JME (JSR 79). Java Specificaion Reques, Sepember 00.. Gudgin M., Hadley M., Rogers T Web Services Addressing.0 Core. WC Recommendaion. hp:// 4. Mira N. 00. SOAP Version. Par 0: Primer. wc Recommendaion. hp:// 5. Bielskis A. A., Dzemydienė D., Denisovas V., Andziulis A., Drungilas D An approach of muli-agen conrol of bio-robos using inelligen recogniion diagnosis of persons wih moving disabiliies// Technological and Economic Developmen of Economy. 5():