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IETCOMPUTINGSERIES34 AI for Emerging Verticals Othervolumesinthisseries: Volume1 KnowledgeDiscoveryandDataMiningM.A.Bramer(Editor) Volume3 TroubledITProjects:PreventionandturnaroundJ.M.Smith Volume4 UMLforSystemsEngineering:Watchingthewheels,2ndEditionJ.Holt Volume5 IntelligentDistributedVideoSurveillanceSystemsS.A.Velastinand P.Remagnino(Editors) Volume6 TrustedComputingC.Mitchell(Editor) Volume7 SysMLforSystemsEngineeringJ.HoltandS.Perry Volume8 ModellingEnterpriseArchitecturesJ.HoltandS.Perry Volume9 Model-BasedRequirementsEngineeringJ.Holt,S.PerryandM.Bownsword Volume13 TrustedPlatformModules:Why,whenandhowtousethemA.Segall Volume14 FoundationsforModel-BasedSystemsEngineering:Frompatternstomodels J.Holt,S.PerryandM.Bownsword Volume15 BigDataandSoftwareDefinedNetworksJ.Taheri(Editor) Volume18 ModelingandSimulationofComplexCommunicationM.A.Niazi(Editor) Volume20 SysMLforSystemsEngineering:Amodel-basedapproach,3rdEdition J.HoltandS.Perry Volume23 DataasInfrastructureforSmartCitiesL.SuzukiandA.Finkelstein Volume24 UltrascaleComputingSystemsJ.Carretero,E.JeannotandA.Zomaya Volume25 BigData-EnabledInternetofThingsM.Khan,S.Khan,A.Zomaya(Editors) Volume26 HandbookofMathematicalModelsforLanguagesandComputation A.Meduna,P.HorácˇekandM.Tomko Volume32 NetworkClassificationforTrafficManagement: Anomalydetection, feature selection,clusteringandclassificationZ.Tari,A.Fahad,A.AlmalawiandX.Yi Volume33 EdgeComputing:Models,technologiesandapplicationsJ.Taheriand S.Deng(Editors) Volume35 BigDataRecommenderSystemsVols.1and2O.Khalid,S.U.Khan, A.Y.Zomaya(Editors) AI for Emerging Verticals Human-robot computing, sensing and networking Edited by Muhammad Zeeshan Shakir and Naeem Ramzan TheInstitutionofEngineeringandTechnology PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom TheInstitutionofEngineeringandTechnologyisregisteredasaCharityinEngland&Wales (no.211014)andScotland(no.SC038698). ©TheInstitutionofEngineeringandTechnology2021 Firstpublished2020 ThispublicationiscopyrightundertheBerneConventionandtheUniversalCopyright Convention.Allrightsreserved.Apartfromanyfairdealingforthepurposesofresearch orprivatestudy,orcriticismorreview,aspermittedundertheCopyright,Designsand PatentsAct1988,thispublicationmaybereproduced,storedortransmitted,inany formorbyanymeans,onlywiththepriorpermissioninwritingofthepublishers,orin thecaseofreprographicreproductioninaccordancewiththetermsoflicencesissued bytheCopyrightLicensingAgency.Enquiriesconcerningreproductionoutsidethose termsshouldbesenttothepublisherattheundermentionedaddress: TheInstitutionofEngineeringandTechnology MichaelFaradayHouse SixHillsWay,Stevenage Herts,SG12AY,UnitedKingdom www.theiet.org Whiletheauthorsandpublisherbelievethattheinformationandguidancegiveninthis workarecorrect,allpartiesmustrelyupontheirownskillandjudgementwhenmaking useofthem.Neithertheauthorsnorpublisherassumesanyliabilitytoanyoneforany lossordamagecausedbyanyerrororomissioninthework,whethersuchanerroror omissionistheresultofnegligenceoranyothercause.Anyandallsuchliability isdisclaimed. Themoralrightsoftheauthorstobeidentifiedasauthorsofthisworkhavebeen assertedbytheminaccordancewiththeCopyright,DesignsandPatentsAct1988. BritishLibraryCataloguinginPublicationData AcataloguerecordforthisproductisavailablefromtheBritishLibrary ISBN978-1-78561-982-3(hardback) ISBN978-1-78561-983-0(PDF) TypesetinIndiabyMPSLimited PrintedintheUKbyCPIGroup(UK)Ltd,Croydon Contents Abouttheeditors xiii Preface xv PARTI Human–robot 1 1 Deeplearningtechniquesformodellinghumanmanipulationand itstranslationforautonomousroboticgraspingwithsoft end-effectors 3 Visar Arapi, Yujie Zhang, Giuseppe Averta, Cosimo Della Santina, and MatteoBianchi 1.1 Introduction 3 1.2 Investigationofthehumanexample 5 1.2.1 Methods 6 1.2.2 Experiments 9 1.3 Autonomousgraspingwithanthropomorphicsofthands 12 1.3.1 Highlevel:deepclassifier 12 1.3.2 Transferringgraspingprimitivestorobots 17 1.3.3 Experimentalsetup 18 1.3.4 Results 21 1.4 Discussionandconclusions 22 Acknowledgement 25 References 26 2 Artificialintelligenceforaffectivecomputing:anemotion recognitioncasestudy 29 Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herráez, andNaeemRamzan 2.1 Introduction 29 2.2 Modelsofhumanaffect 30 2.2.1 Discretemodelsofaffect 30 2.2.2 Continuous(dimensional)modelsofaffect 31 2.3 Previousworkonemotionrecognition 34 2.4 Datasetsforemotionrecognition 35 2.5 Proposedmethodology 36 2.5.1 Connectivityfeatures 36 2.5.2 Energyfeatures 37 2.5.3 Dimensionalityreduction 37 vi AIforemergingverticals 2.6 Experimentalresults 38 2.7 Conclusionsanddiscussion 40 Acknowledgement 41 References 41 3 Machinelearning-basedaffectdetectionwithinthecontextof human–horseinteraction 45 TurkeAlthobaiti,StamosKatsigiannis,DauneWest,HassanRabah, andNaeemRamzan 3.1 Introduction 45 3.2 Background 47 3.3 Experimentalprotocol 48 3.3.1 Fieldexperimentsetting 48 3.3.2 Experimentaldataacquisition 49 3.3.3 Self-reportingofemotionalstate 50 3.3.4 Participants 51 3.4 Analysisofcaptureddata 51 3.4.1 Pre-processingofphysiologicalsignals 51 3.4.2 Extractionoffeaturesfromphysiologicalsignals 52 3.4.3 Emotionlabels 53 3.5 Experimentalresults 55 3.6 Discussion 57 3.7 Conclusion 58 References 58 4 Robotintelligenceforreal-worldapplications 63 EleftheriosTriantafyllidis,ChuanyuYang,ChristopherMcGreavy, WenbinHu,andZhibinLi 4.1 Introduction 63 4.2 Novelroboticapplicationsinlocomotion 64 4.2.1 Deep reinforcement learning for dynamic locomotion of bipedalrobots 64 4.2.2 Learningfromhumans 70 4.3 Novelroboticapplicationsinhuman-guidedmanipulation 74 4.3.1 Background,trendsandchallenges 74 4.3.2 Discussionandfrontiersinhuman-guidedmanipulation 81 4.4 Novelroboticapplicationsinfullyautonomousmanipulation 82 4.4.1 Background 83 4.4.2 Relatedwork 84 4.4.3 Reaching,graspingandre-grasping 86 4.5 Conclusion 87 References 87 Contents vii 5 VisualobjecttrackingbyquadrotorAR.Droneusingartificial neuralnetworksandfuzzylogiccontroller 101 KamelBoudjit,CherifLarbesandNaeemRamzan 5.1 Introduction 101 5.2 Systemoverview 102 5.2.1 Controlsystem 103 5.2.2 Quadrotordynamicmodel 104 5.3 Fuzzy-logic-basedidentificationandtargettracking 105 5.3.1 Simulationstudies 106 5.3.2 Experimentalsetup 113 5.4 Artificial neural networks (ANN) for target identification and trackingusingaquadrotor 117 5.4.1 Designoftheneuralnetwork 119 5.4.2 Simulationresults 123 5.4.3 Experimentalvalidationandresults 127 5.5 Conclusion 129 References 130 PARTII Network 133 6 Predictivemobilitymanagementincellularnetworks 135 MetinÖztürk,PauloValenteKlaine,SajjadHussain,and MuhammadAliImran 6.1 Introduction 135 6.1.1 Thepathtowards5G 136 6.1.2 5Genablersandchallenges 137 6.1.3 Issuesfromnewtechnologies 138 6.1.4 Chapterobjectives 139 6.1.5 Organisationofthechapter 139 6.2 Mobilitymanagementincellularnetworks 139 6.3 Predictivemobilitymanagement 142 6.3.1 Stateoftheartinpredictive mobilitymanagement 145 6.4 AdvancedMarkov-chain-assistedpredictive mobilitymanagement 147 6.4.1 Markov-chain-basedmobilityprediction 147 6.4.2 ProblemwiththeconventionalMarkovchains 148 6.4.3 Introductionto3Dtransitionmatrix 149 6.4.4 Performanceevaluation 149 6.5 Summary 152 References 152 viii AIforemergingverticals 7 Artificial intelligence and data analytics in 5G and beyond-5G wirelessnetworks 157 MaziarNekovee,DehaoWu,YueWangandMehrdadShariat 7.1 Introduction 157 7.2 CasestudiesofAIin5Gwirelessnetworks 159 7.2.1 Casestudy1:AIforcellselection 159 7.2.2 Casestudy2:AIfor5Gfronthaul 163 7.2.3 Casestudy3:AIforcoexistenceofmultipleradioaccess technologies 164 7.3 Dataanalyticsin5G 167 7.4 Industryandstandardactivities 168 7.4.1 Openstandardsrequired 168 7.4.2 Achievementsandactivatesofstandardization 169 7.5 Challengesandopenquestions 169 7.5.1 Bigdataorsmalldata 170 7.5.2 Centralizedordistributedlearning 170 7.6 Conclusions 171 References 171 8 DeepQ-network-basedcoverageholedetectionforfuturewireless networks 173 ShahriarAbdullahAl-Ahmed,MuhammadZeeshanShakir, andQasimZeeshanAhmed 8.1 Introduction 173 8.1.1 Motivationandbackground 174 8.2 Machinelearning 175 8.2.1 Reinforcementlearning 176 8.2.2 Deepreinforcementlearning 177 8.3 Systemmodel 177 8.4 DQN-basedcoverageholedetection 179 8.4.1 DQNcomponentstodetectcoveragehole 180 8.4.2 DQNprinciplestodetectcoveragehole 181 8.5 Simulationresultsanddiscussion 183 8.6 Conclusions 186 References 187 9 Artificialintelligenceforlocalizationof ultrawidebandwidth(UWB)sensornodes 189 FuhuChe,AbbasAhmed,QasimZeeshanAhmed,and MuhammadZeeshanShakir 9.1 Introduction 189 9.2 Indoorpositioningsystem 191 9.2.1 UWBindoorpositioningsystem 192 Contents ix 9.3 UWBrangingaccuracyevaluation 194 9.3.1 NaïveBayes(NB)classifier 194 9.3.2 Receiveroperatingcharacteristic 195 9.4 Implementationandevaluation 196 9.4.1 Experimentsetupandenvironment 198 9.4.2 NBandROCresults 198 9.5 Conclusion 201 References 201 10 ACascadedMachineLearningApproachforindoorclassification andlocalizationusingadaptivefeatureselection 205 MohamedI.AlHajri,NazarT.AliandRaedM.Shubair 10.1 Introduction 205 10.2 Indoorradiopropagationchannel 206 10.2.1 CharacteristicsofRFindoorchannel 206 10.2.2 DesignconsiderationsfortheRFindoorchannel 207 10.3 Datacollectionphase:practicalmeasurementscampaign 207 10.4 Signaturesofindoorenvironment 208 10.4.1 PrimaryRFfeatures 208 10.4.2 HybridRFfeatures 209 10.5 Spatialcorrelationcoefficient 210 10.6 Machinelearningalgorithms 213 10.6.1 Decisiontrees 213 10.6.2 Supportvectormachine 213 10.6.3 k-Nearestneighbor 214 10.7 CascadedMachineLearningApproach 215 10.7.1 Machinelearningforindoorenvironmentclassification 215 10.7.2 Machinelearningforlocalizationpositionestimation 216 10.8 Conclusion 219 References 220 PARTIII Sensing 225 11 EEG-basedbiometrics:effectsoftemplateageing 227 Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herraez andNaeemRamzan 11.1 Introduction 227 11.2 Background 228 11.2.1 Biometrics 228 11.2.2 Electroencephalography 230 11.2.3 EEGinbiometrics 232 11.3 Dataacquisitionandexperimentalprotocol 232 11.3.1 Stimuli 232 11.3.2 Experimentalprotocol 233