A System Framework for Decision Support in Ambient Intelligence
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A System Framework for Decision Support in Ambient Intelligence

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Yamabe's presentation slides on his final phd oral defense.

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A System Framework for Decision Support in Ambient Intelligence A System Framework for Decision Support in Ambient Intelligence Document Transcript

  • A System Framework for Decision Support in Ambient Intelligence Tetsuo Yamabe Distributed and Ubiquitous Computing Lab., Waseda University2010 12 22 1 Preface2010 12 22 2
  • Mobile Activity-based Pedestrian Micro-Pricing Navigation Systems Case Study #1 Case Study #3 Augmented Map Guide Traditional on Games Public Displays Case Study #2 Case Study #4 p. 32010 12 22 3 UbiComp’09 MobiQuitous’08 Persuasive’10 Case Study #1 Case Study #3 TEI’10, 11 FNG’10 ICPS’10 IoT’10 Case Study #2 Case Study #4 p. 42010 12 22 4
  • Case study #3 Decision inducement with Activity-based Micro-Incentives Case studies ( ) Case study #2 Citron Decision training with A context acquisition augmented traditional framework for mobile games sensor devices p. 52010 12 22 5 Outline 1. Introduction 2. Ambient Decision Support Systems • ADSS System Framework 3. Case Studies • Decision Training with Augmented Traditional Games • Decision Inducement with Activity-based Micro-Incentives 4. Discussion 5. Conclusion and future work p. 62010 12 22 6
  • Introduction2010 12 22 7 Decisions in everyday life • Life is the outcome of decisions. • “What’s for dinner tonight?” - Japanese, Chinese, Western-style... • “How to go to office?” - by bus, train, walk... • “Which university’s exam should I try?” - Waseda, Keio, Toudai... p. 82010 12 22 8
  • Limitations and fallacies in decision making • Perceptual ability • Skill, knowledge level • Heuristics • Bias • Human error p. 92010 12 22 9 Decision Support Systems (DSS) “A DSS is an interactive computer based system that helps decision makers utilize data and models to solve unstructured problems.” * *Adapted from G. A. Gorry and M. S. S. Morton. A framework for management information systems. Sloan Management Review, 13(1):50–70, 1971. p.102010 12 22 10
  • Decision support types 1. Decision aiding • Capability enhancement • Process automation • Performance improvement • Error correction and prevention 2. Decision training • Evolution to the next expert level W. W. Zachary and J. M. Ryder. Decision support systems: Integrating decision aiding and decision training. Handbook of Human-Computer Interaction (Second Edition), pages 1235 – 1258, 1997. p.112010 12 22 11 Traditional DSS ADSS Organizational Personal Business Personal use → Stationary Mobile PC Environments p.122010 12 22 12
  • Ambient Decision Support Systems2010 12 22 13 Ambient Intelligence (AmI) “AmI is a vision of the Information Society where the emphasis is on greater user-friendliness, more efficient services support, user- empowerment, and support for human interactions. People are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects and an environment that is capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way.” [Ducatel et. al, 2001] p.142010 12 22 14
  • AmI key features and technologies Key features Enabling technologies Embedded Very unobtrusive hardware A seamless mobile/fixed Context aware communications infrastructure Dynamic and massively Personalized distributed device networks Adaptive Natural feeling human interfaces Anticipatory Dependability and security p.152010 12 22 15 Decision support in AmI •Ambient Decision Support Systems (ADSS) assist in an individual’s everyday decision making. • Cognitive capability enhancement with sensors and actuators • Sustainable decision support through implicit/explicit interaction • Large-scale knowledge aggregation on cloud infrastructure p.162010 12 22 16
  • DSS system architecture AmI DSS Context information Database acquired by sensors Mgmt System User Dialog Knowledge User Interface Mgmt System Engine Model base Mgmt System Surrounded by Augmented reality: heterogeneous seamlessly integrated smart objects feedback Distributed over the cloud network p.172010 12 22 17 Live activity logging and realtime multimodal feedback Visual presentation of the record for further analysis p.182010 12 22 18
  • ADSS key requirements • Decision need to be made for not well structured problems while adapting to the contextual transition in social activities. • Behavioral considerations of decision making have been ignored by those who create and build DSS. • It is important to understand decision making process from cognitive perspective. 1. Endsley’s Situation Awareness model [Endsley, 1995] 2. Rasmussen’s SRK behavior model [Rasmussen, 1983] p.192010 12 22 19 • !"#$%&!()*)+,-,%#! • !./%&01)(&!2&$,3/! • !"%0&$$!4!5607-6)2! • !86*-&9,%#! • !:;%6)<6/ =)$7>"#$%&!?)(%60$ E""FG1$H :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 !"B.>.B27)C+3$%- • );$7-)<)$6=,+(>,-) • )?%,+$"+,*($"-) !"#$%&($")*%$+,--."/) )@89*,+3($"-A &,+0".-&- 5$"/)3,%&)) 123$&(+.34 &,&$%4)-3$%,-Endsley’s Situation Awareness model • )16.7.(,-) • )89*,%.,"+,) • ):%."."/M. R. Endsley. Toward a theory of situation awareness indynamic systems. Human Factors: The Journal of the HumanFactors and Ergonomics Society, 37(1):32–64, 1995. p.202010 12 22 20
  • • !"#$%&!()*)+,-,%#! • !./%&01)(&!2&$,3/! • !"%0&$$!4!5607-6)2! • !86*-&9,%#! • !:;%6)<6/ =)$7>"#$%&!?)(%60$ E""FG1$H :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 !"B.>.B27)C+3$%- • );$7-)<)$6=,+(>,-) • )?%,+$"+,*($"-) !"#$%&($")*%$+,--."/) )@89*,+3($"-A &,+0".-&- 5$"/)3,%&)) 123$&(+.34 &,&$%4)-3$%,-Endsley’s Situation Awareness model • )16.7.(,-) • )89*,%.,"+,) • ):%."."/M. R. Endsley. Toward a theory of situation awareness indynamic systems. Human Factors: The Journal of the HumanFactors and Ergonomics Society, 37(1):32–64, 1995. p.212010 12 22 21 • !"#$%&!()*)+,-,%#! • !./%&01)(&!2&$,3/! • !"%0&$$!4!5607-6)2! • !86*-&9,%#! • !:;%6)<6/ =)$7>"#$%&!?)(%60$ E""FG1$H :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 !"B.>.B27)C+3$%- • );$7-)<)$6=,+(>,-) • )?%,+$"+,*($"-) !"#$%&($")*%$+,--."/) )@89*,+3($"-A &,+0".-&- 5$"/)3,%&)) 123$&(+.34 &,&$%4)-3$%,-Endsley’s Situation Awareness model • )16.7.(,-) • )89*,%.,"+,) • ):%."."/M. R. Endsley. Toward a theory of situation awareness indynamic systems. Human Factors: The Journal of the HumanFactors and Ergonomics Society, 37(1):32–64, 1995. p.222010 12 22 22
  • • 250*61)7.(2 &),#0/# ,#+&"&2%825#4%=%:"#$%825#4 ,#+&"&2%5*(*%=%+2(#>(%&)2 • 289(0*":0,2 • 2&(/)##2;2<0/$10"=2 A)(<0/$ • 2>/04)##.,?260<)/2 Task • 2&),#.,?24"6"@.1.(2 .0./ 1234#56#% ,0./ ADSS #6&# • 2A)(<0/$24"6"@.1.(2 B.#61"# • 2B,"*.4#2 System Environments • 2C91:("#$.,?2 84(9"(0/# • 2&.(9":0,"1240,=.:0,2 ,-./ D,()/"4:-)2 0@E)4(# 789&#(%% !"#$%&(#$)*+# !"#$%&#()*%+,-./0,*),("123"4(0/# ,#+&"&2%/:;;2$(%/<"(#8 !"#$%$&(/$1$2( ADSS System Framework =."# M""NI1$O !"#$%$&(%)**&+,($-&+./0& :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- User $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 ;(N/3/N01+)M1$-#. (Situation Awareness model) • )G1+.)H)I8"$&3".) • )!#"$($"%&(.) ;(*#,1&()%#$"../(E) D"$/./()-#1/(/(E) )JCK%"$-1&(.L ,"$61(/.,. 2(E)-"#,)) • )>I/+/&".) >0-,1&$/-F ,",#F).-#". • )CK%"#/"($") p.232010 12 22 23 • 250*61)7.(2 &),#0/# ,#+&"&2%825#4%=%:"#$%825#4 ,#+&"&2%5*(*%=%+2(#>(%&)2 • 289(0*":0,2 • 2&(/)##2;2<0/$10"=2 A)(<0/$ • 2>/04)##.,?260<)/2 1234#56#% • 2&),#.,?24"6"@.1.(2 .0./ ,0./ #6&# • 2A)(<0/$24"6"@.1.(2 B.#61"# • 2B,"*.4#2 • 2C91:("#$.,?2 84(9"(0/# • 2&.(9":0,"1240,=.:0,2 ,-./ D,()/"4:-)2 0@E)4(# 789&#(%% !"#$%&(#$)*+# !"#$%&#()*%+,-./0,*),("123"4(0/# ,#+&"&2%/:;;2$(%/<"(#8 !"#$%$&(/$1$2( =."# M""NI1$O !"#$%$&(%)**&+,($-&+./0& :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 ;(N/3/N01+)M1$-#. • )G1+.)H)I8"$&3".) • )!#"$($"%&(.) ;(*#,1&()%#$"../(E) D"$/./()-#1/(/(E) )JCK%"$-1&(.L ,"$61(/.,. 2(E)-"#,)) • )>I/+/&".) >0-,1&$/-F ,",#F).-#". • )CK%"#/"($") p.242010 12 22 24
  • • 250*61)7.(2 &),#0/# ,#+&"&2%825#4%=%:"#$%825#4 ,#+&"&2%5*(*%=%+2(#>(%&)2 • 289(0*":0,2 • 2&(/)##2;2<0/$10"=2 A)(<0/$ • 2>/04)##.,?260<)/2 1234#56#% • 2&),#.,?24"6"@.1.(2 .0./ ,0./ #6&# • 2A)(<0/$24"6"@.1.(2 B.#61"# • 2B,"*.4#2 • 2C91:("#$.,?2 84(9"(0/# • 2&.(9":0,"1240,=.:0,2 ,-./ D,()/"4:-)2 0@E)4(# 789&#(%% !"#$%&(#$)*+# !"#$%&#()*%+,-./0,*),("123"4(0/# ,#+&"&2%/:;;2$(%/<"(#8 !"#$%$&(/$1$2( =."# M""NI1$O !"#$%$&(%)**&+,($-&+./0& :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 ;(N/3/N01+)M1$-#. • )G1+.)H)I8"$&3".) • )!#"$($"%&(.) ;(*#,1&()%#$"../(E) D"$/./()-#1/(/(E) )JCK%"$-1&(.L ,"$61(/.,. 2(E)-"#,)) • )>I/+/&".) >0-,1&$/-F ,",#F).-#". • )CK%"#/"($") p.252010 12 22 25 • 250*61)7.(2 &),#0/# ,#+&"&2%825#4%=%:"#$%825#4 ,#+&"&2%5*(*%=%+2(#>(%&)2 • 289(0*":0,2 • 2&(/)##2;2<0/$10"=2 A)(<0/$ • 2>/04)##.,?260<)/2 1234#56#% • 2&),#.,?24"6"@.1.(2 .0./ ,0./ #6&# • 2A)(<0/$24"6"@.1.(2 B.#61"# • 2B,"*.4#2 • 2C91:("#$.,?2 84(9"(0/# • 2&.(9":0,"1240,=.:0,2 ,-./ D,()/"4:-)2 0@E)4(# 789&#(%% !"#$%&(#$)*+# !"#$%&#()*%+,-./0,*),("123"4(0/# ,#+&"&2%/:;;2$(%/<"(#8 !"#$%$&(/$1$2( =."# M""NI1$O !"#$%$&(%)**&+,($-&+./0& :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 ;(N/3/N01+)M1$-#. • )G1+.)H)I8"$&3".) • )!#"$($"%&(.) ;(*#,1&()%#$"../(E) D"$/./()-#1/(/(E) )JCK%"$-1&(.L ,"$61(/.,. 2(E)-"#,)) • )>I/+/&".) >0-,1&$/-F ,",#F).-#". • )CK%"#/"($") p.262010 12 22 26
  • Case Studies2010 12 22 27 Decision Training with Augmented Traditional Games CS#2(Chapter 5)2010 12 22 28
  • System concept • Objective • Support decision making in traditional games, by providing feedback/feedforward in a seamlessly integrated way. • Decision support ADSS • Visualize/sonify the information that novices Visualization cannot well recognize, in order to improve the situation awareness on current status. User p.292010 12 22 29 Ex. 1) Augmented Go T. Iwata, T. Yamabe, M. Polojarvi, and T. Nakajima. 2010. Traditional games meet ICT: a case study on go game augmentation. In Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction (TEI 10). ACM, , 237-240. p.302010 12 22 30
  • Characteristics as an ADSS Characteristics Technologies Structured rules and restrictions Augmented reality Stationary Tangible interaction Immersion Emotional Context awareness p.312010 12 22 31 Ex. 2) EmoPoker • Improve decision performance under uncertainty. • “It is well established that intense drive states such as hunger, pain, sexual arousal, drug cravings, and sleep deprivation produce breakdowns in self-control and increase people’s willingness to take risks in order to alleviate the drive state.” * • Biofeedback for improving self-control of emotional arousal. • Auditory feedback which emulates heartbeat sound of the player * Adapted from M. Pham. Emotion and rationality: A critical review and interpretation of empirical evidence. Review of General Psychology, Jan 2007. p.322010 12 22 32
  • EmoPoker System T. Yamabe, I. Kosunen, I. Ekman, LA. Liikkanen, K. Kuikkaniemi, and T. Nakajima, Biofeedback Training with EmoPoker: Controlling Emotional Arousalfor Better Poker Play. Fun and Games Conference 2010 (Fun and Games’10) p.332010 12 22 33 EmoPoker System Physiological Emotional arousal response detection Player Mobile PC Biofeedback p.342010 12 22 34
  • Biofeedback user study fEMG (facial electromyography) • 6 nodes to face • Positive/negative emotions EDA (electrodermal activity) • 2 nodes to fingers • Arousal level RESP (respiration)! • 1 node around chest HR (Heart rate) • 1 node around chest • Emotional arousal p.352010 12 22 35 Biofeedback user study •5 min offline poker play x 8 rounds = 40 min in total • 4 conditions x 2 sets • Feedback {ON, OFF} x Tilt mode {ON, OFF} •8 subjects in Finland • Volunteers gathered by mailing list p.362010 12 22 36
  • Results •5 subjects data could be used for statistical analysis. • Linear Mixed Model for repeated measurements. • The tilt mode increased emotional arousal (p<.05). • The biofeedback slightly decreased the arousal. p.372010 12 22 37 Findings from the experiment • Informativeness depends on the expertise level. • “Tell as it is” is not useful especially for novice players. • Preliminary knowledge helps to interpret the ambient message. • Biofeedback design to induce autonomous response • Breathing pattern indication for calm heated brain. p.382010 12 22 38
  • Decision Inducement with Activity-based Micro-Incentives CS#3(Chapter 6)2010 12 22 39 System concept • Objective • Induce consumers’ decision behavior towards more desirable decision patterns with incentives. ADSS • Decision support Incentives • Induce desirable decisions by providing User continuos economic incentives to activities. p.402010 12 22 40
  • Pricing towards better decisions Adapted from the web page of The General Insurance Association of Japan (http://www.sonpo.or.jp/protection/insyu/) p.412010 12 22 41 Activity-based Micro-Pricing system p.422010 12 22 42
  • System architecture p.432010 12 22 43 Characteristics as an ADSS Characteristics Technologies Semi-/non structured Mobile Context awareness Economic incentives Mobile payment Cognitive bias p.442010 12 22 44
  • People subjectively frame economic transactions in their mind. • Loss-aversion decision making • Greater impact on trivial sums of money Kahneman’s Prospect theoryD. Kahneman and A. Tversky. Prospect theory: An analysis ofdecision under risk. Econometrica, 47(2):263–292, Mar 1979. p.452010 12 22 45 UbiPayment and UbiRebate model Small initial cost Large initial cost Micro charge Micro rebating User User cost cost time time p.462010 12 22 46
  • User study 1. Psychological test for asymmetric transaction effect evaluation 2. Simulation setup of the micro-pricing world p.472010 12 22 47 User study #1 1. Payment: No initial cost (5 JPY payment per 2 seconds for using the tool) 2. Rebate: 200 JPY as initial cost (5 JPY rebate per 2 seconds for NOT using the tool) p.482010 12 22 48
  • Results • 12 university students joined (male: 11, female:1, age: 22-25) • 60 seconds examination for each round • Participants used the tool longer time in the rebate case than the payment case. • Rebates might strongly encourage the participants to use the tool, even though a bigger initial cost was withdrawn. Payment Rebate Transferred circles (num) 60.91 61.33 Time taken to use the tool (sec) 17.00 (28%) 38.33 (63%) Averaged experimental result of the flash application test. p.492010 12 22 49 User study #2 p.502010 12 22 50
  • User study #2 p.512010 12 22 51 Findings from the experiment • Feedback design amplifies psychological impact. • Frequent notification increases cognitive load and obtrusiveness. • Automatic payment transaction elicits anxiety and reluctance. • Need to affect attitude to achieve sustainable behavior change. • Persuasive messages induce reasoning and higher elaboration. • Hybrid incentive design with social incentives. p.522010 12 22 52
  • Discussion2010 12 22 53 • 250*61)7.(2 &),#0/# ,#+&"&2%825#4%=%:"#$%825#4 ,#+&"&2%5*(*%=%+2(#>(%&)2 • 289(0*":0,2 • 2&(/)##2;2<0/$10"=2 Attention A)(<0/$ • 2>/04)##.,?260<)/2 1234#56#% • 2&),#.,?24"6"@.1.(2 .0./ ,0./ #6&# • 2A)(<0/$24"6"@.1.(2 B.#61"# • 2B,"*.4#2 • 2C91:("#$.,?2 84(9"(0/# • 2&.(9":0,"1240,=.:0,2 ,-./ D,()/"4:-)2 0@E)4(# 789&#(%% !"#$%&(#$)*+# !"#$%&#()*%+,-./0,*),("123"4(0/# ,#+&"&2%/:;;2$(%/<"(#8 !"#$%$&(/$1$2( =."# M""NI1$O Emotion !"#$%$&(%)**&+,($-&+./0& :;<=><;?@)>A>BC@C:: !"#$"%&()*) 5,%#"6"(./()*) !#8"$&()*) !"#*#,1($") :-1-")*)-6") "+","(-.)/() $0##"(-)./-01&() *0-0#").-1-0.) D"$/./( *) "(3/#(,"(- $0##"(-)./-01&() 1$&(. 2"3"+)4 2"3"+)7 2"3"+)9 ;(N/3/N01+)M1$-#. Motivation • )G1+.)H)I8"$&3".) • )!#"$($"%&(.) )JCK%"$-1&(.L ;(*#,1&()%#$"../(E) ,"$61(/.,. D"$/./()-#1/(/(E) 2(E)-"#,)) • )>I/+/&".) >0-,1&$/-F ,",#F).-#". • )CK%"#/"($") p.542010 12 22 54
  • Irrational decision making The sense of immersion enables to Arousal perceive more decision information and elicit emotional response by the experience. Emotion In anticipation of better emotional feeling, motivation is reinforced towards Attention Human error particular behavior. Motivation Cognitive load Attitude Stronger motivation directs more Negative behavior attention to the decision problem. p.552010 12 22 55 Other design issues • [User] Simplified ambient media decreases cognitive load, but requires skill and knowledge for interpretation. • [System] Automaticity beyond a user’s consciousness and control elicits negative emotional arousal. • [Developer] ADSS developers could lack of expertise for satisfying a variety of user’s requirement. • [Ethic] Decision control could ethically be a problem. p.562010 12 22 56
  • Conclusion and future work2010 12 22 57 Conclusion • As the main contribution of this work, we identified practical design guidelines for ADSS development, from the four case studies designed upon our system framework. • Whereas decision support is an essential aspect for most AmI services, human decision making mechanism is often discussed apart from the system design. • The ADSS framework will assist in service developers to design a system with directing polite attention to psychological issues. p.582010 12 22 58
  • Future work • Next iteration in more practical application domains. 1. Automobile - highly dynamic - attention 2. Financial activities - highly uncertain - emotion 3. Social infrastructure - highly complicated - motivation p.592010 12 22 59 Seam-full vs Seamless Control vs Support Crowd vs Individual p.602010 12 22 60
  • Selected publications • T. Yamabe, I. Kosunen, I. Ekman, LA. Liikkanen, K. Kuikkaniemi, and T. Nakajima, Biofeedback Training with EmoPoker: Controlling Emotional Arousalfor Better Poker Play. Fun and Games Conference 2010 (Fun and Games’10, WIP paper) • T. Yamabe, Y. Washio, S. Matsuzawa, T. Nakajima, Empowering End-users to Find Point-of-interests with a Public Display, In Proc. of the 2010 International Conference on Pervasive Services (ICPS’10, full paper) • T. Yamabe, V. Lehdonvirta, H. Ito, H. Soma, H. Kimura, and T. Nakajima, Activity-Based Micro-Pricing: Realizing Sustainable Behavior Changes Through Economic Incentives. In Proc. of The Fifth International Conference on Persuasive Technology (Persuasive’10, full paper) • T.Yamabe,V. Lehdonvirta, H. Ito, H. Soma, H. Kimura, and T. Nakajima, Applying Pervasive Technologies to Create Economic Incentives that Alter Consumer Behavior. In Proc. of The 11th International Conference on Ubiquitous Computing (UbiComp’09, full paper, acceptance rate: 12.35%) • T. Yamabe and T. Nakajima, Possibilities and Limitations of Context Extraction in Mobile Devices: Experiments with a Multi-sensory Personal Device, International Journal of Multimedia and Ubiquitous Engineering. 2009. vol.4(4) • T.Yamabe, K. Takahashi, and T. Nakajima, Towards Mobility Oriented Interaction Design: Experiments in Pedestrian Navigation on Mobile Devices, In Proc. of The Fifth Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous’08, full paper, acceptance rate: 17.14%) • T. Yamabe, A. Takagi, and T. Nakajima. 2005. Citron: A Context Information Acquisition Framework for Personal Devices, In Proc. of the 11th IEEE international Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05, full paper) p.612010 12 22 61 Thank you!2010 12 22 62