COMPUTATIONAL MODELS AND ANALYSES OF HUMAN MOTOR PERFORMANCE IN HAPTIC MANIPULATION by MICHAEL J. FU Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY May 2011 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Michael John Fu ______________________________________________________ Doctor of Philosophy candidate for the ________________________________degree *. Prof. M. Cenk Cavusoglu (signed)_______________________________________________ (chair of the committee) Prof. Wyatt S. Newman ________________________________________________ Prof. Kenneth A. Loparo ________________________________________________ Prof. Wei Lin ________________________________________________ Prof. Roger D. Quinn ________________________________________________ ________________________________________________ March 31, 2011 (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. Copyright c 2011 by Michael John Fu All rights reserved Contents List of Tables v List of Figures vii Acknowledgements viii Abstract ix 1 Introduction 1 1.1WhatisHaptics?............................. 1 1.2Contributions............................... 3 2 Background 4 2.1VirtualEnvironmentImmersionTechniques.............. 4 2.1.1 FishTankDisplay........................ 5 2.2EffectofImmersiononTaskPerformance................ 6 2.2.1 StereographicRendering..................... 6 2.2.2 PhysicalvsVirtualTasks.................... 8 2.2.3 VisualandHapticWorkspaceCo-location........... 10 2.2.4 EffectofVisualRotationsonTaskPerformance........ 11 2.3Fitts’Law................................. 13 2.3.1 Comparing Experimental Conditions: Throughput . ...... 14 i 2.3.2 3DExtensions........................... 16 2.4HumanOperatorModels......................... 16 3 Arm-and-Hand Dynamics and Variability Modeling 18 3.1Methods.................................. 20 3.1.1 Input Signals Used in the Human Experiment . ...... 20 3.1.2 Subjects.............................. 21 3.1.3 Equipment............................. 22 3.1.4 ArmModelExperimentParadigm................ 23 3.1.5 ArmDynamicsModelStructure................. 26 3.1.6 Structured Variability ...................... 29 3.1.7 Unstructured Variability Model ................. 29 3.2Measured-DynamicsModelResults................... 31 3.2.1 ArmDynamicsModelIdentificationResults.......... 31 3.2.2 Variability Results ........................ 32 3.3Discussion................................. 34 3.3.1 ComparisonwithPreviousArmModelParameters...... 34 3.3.2 Grip-Force-DependentModels.................. 40 3.3.3 Structured Variability ...................... 41 3.3.4 Unstructured Variability ..................... 42 4 Arm Model ID Without Force Transducers 44 4.1Methods.................................. 45 4.1.1 PhantomandArmDynamicsModelsStructure........ 45 4.1.2 Structured Variability ...................... 49 4.1.3 Unstructured Variability Model ................. 49 4.2DerivationofArm-OnlyExperimentalFrequencyResponse...... 50 4.2.1 Subjects.............................. 52 ii 4.2.2 ArmModelExperimentParadigm................ 52 4.3Measured-DynamicsModelResults................... 52 4.3.1 ArmDynamicsModelIdentificationResults.......... 52 4.3.2 Variability Results ........................ 53 4.4Discussion................................. 56 4.4.1 ComparedtoResultsUsingForceSensors........... 60 5 Evaluation of 3D Fitts’ Task in Physical and Virtual Environments 62 5.0.2 StudyObjectives......................... 65 5.1PerformanceMeasuresforAnalysis................... 65 5.1.1 Throughput ............................ 66 5.1.2 End-pointError.......................... 66 5.1.3 NumberofCorrectiveMovements................ 67 5.1.4 Efficiency............................. 67 5.1.5 InitialMovementError...................... 68 5.1.6 PeakVelocity........................... 68 5.1.7 AccountingforEffectofIDonPerformanceMeasures..... 69 5.2Methods.................................. 69 5.2.1 Equipment............................. 69 5.2.2 Subjects.............................. 74 5.2.3 ExperimentParadigms...................... 74 5.3Results................................... 79 5.3.1 Throughput ............................ 79 5.3.2 End-PointError.......................... 83 5.3.3 NumberofCorrectiveMovements................ 84 5.3.4 Efficiency............................. 87 5.3.5 PeakVelocity........................... 89 5.3.6 InitialMovementError...................... 92 iii 5.4Discussion................................. 94 5.4.1 Realvs.Non-colocatedVEvs.Co-locatedVE......... 96 5.4.2 EffectofVisualRotations.................... 98 5.4.3 VESystemDesignImplications................. 100 6 Conclusions 101 6.1Arm-and-handDynamicsModeling................... 101 6.2ReachinginVirtualEnvironments.................... 102 6.3FutureResearchProblems........................ 103 Appendices 105 A Arm Model Derivation 105 B End-effector Inertia for the Phantom Premium 1.5a 106 Related Publications 110 Bibliography 111 iv List of Tables 3.1ArmStructureParameters–GripForceDependentModels...... 31 3.2NominalArmModelParameters..................... 32 3.3 Structured Variability - Arm Structure Parameter Statistics ..... 32 3.4 Unstructured Variability Model Poles and Zeroes ........... 35 3.5ArmModelParametersfromLiterature................. 39 4.1NoF/TSensor:GripForceDependentArmModelParameters.... 53 4.2 No F/T Sensor: Structured Variability Statistics ............ 55 4.3NoF/TSensor:NominalArmModelParameters........... 56 4.4 No F/T Sensor: Unstructured Variability Model Parameters ..... 59 5.1TargetList................................ 75 5.2 Significant Multiple Comparisons – Throughput ............ 80 5.3SignificantMultipleComparisons–End-PointError.......... 84 5.4SignificantMeansComparisons–CorrectiveMovementOffsets.... 87 5.5SignificantMeansComparisons–CorrectiveMovementSlopes.... 87 5.6SignificantMeansComparisons–Efficiency............... 89 5.7SignificantMeansComparisons–PeakVelocityOffset......... 92 5.8SignificantMeansComparisons–PeakVelocitySlope......... 92 5.9SignificantMeansComparisons–InitialMovementError....... 94 5.10 Performance Means and (Std. Dev.) Normalized to Real and 0◦ ... 96 v List of Figures 1.1HapticInterfaceDevices......................... 2 2.1FishTankDisplaySetup......................... 5 2.2 Throughput Example ........................... 15 3.1SystemIdentificationExperimentArmConfiguration......... 22 3.2SystemIdentificationGraphicalUserInterface............. 24 3.3F/TSensorArmModeling:ArmModelFreeBodyDiagram..... 25 3.4F/TSensorArmModeling:Coherence................. 28 3.5 F/T Sensor Arm Modeling: Bode Plots for the Grip-force Dependent ArmDynamicsModels.......................... 33 3.6 F/T Sensor Nominal Arm Model and Unstructured Variability Fits . 36 3.7 F/T Sensor Arm Modeling: Unstructured Variability Models ..... 37 3.8F/TSensorArmModeling:ComparisonofArmModels....... 38 4.1ClosedLoopArmModelBlockDiagram................ 45 4.2ArmModelFreeBodyDiagram..................... 46 4.3 Bode Plots for the Grip-force Dependent Arm Plus Phantom Dynamics Models................................... 54 4.4NoF/TSensor:NominalArm-OnlyModels.............. 57 4.5 No F/T Sensor: Unstructured Variability Fits ............. 58 4.6NoF/TSensorArmModeling:ComparisonwithF/TModels.... 60 vi 5.1FishTankDisplaySetup......................... 70 5.2AllPhysicalTargets........................... 71 5.3FishTankDisplaySetup......................... 72 5.4FishTankUserPosition......................... 73 5.5ExperimentSetupforPhysicalTargets................. 76 5.6FishTankDisplayNon-colocatedSetup................. 77 5.7 Virtual User Interface (0–315◦ rotation)................. 78 5.8 Fish Tank: Throughput Linear Regression ............... 80 5.9 Fish Tank: Throughput Linear Regression R2 Histogram....... 81 5.10 Fish Tank: Throughput Boxplots . ................. 82 5.11FishTank:End-pointErrorBoxplots.................. 85 5.12FishTank:CorrectiveMovementsLinearRegression......... 86 5.13 Fish Tank: Corrective Movements Linear Regression R2 Histogram . 86 5.14FishTank:CorrectiveMovementsBoxplots.............. 88 5.15FishTank:EfficiencyBoxplots...................... 90 5.16FishTank:PeakVelocityLinearRegression.............. 91 5.17 Fish Tank: Peak Velocity Linear Regression R2 Histogram...... 91 5.18FishTank:PeakVelocityBoxplots................... 93 5.19FishTank:InitialMovementErrorBoxplots.............. 95 vii Acknowledgements Thank you, Prof. M. Cenk C¸avu¸so˘glu, for the opportunity of being part of such a great lab. Your steadfastness and encouragement have made all the difference in my career as a graduate student. I don’t know how many times I’ve walked into your office in despair, but left with hope. Te¸sekk¨ur ederim, Drs. Ozkan¨ and Ebru Bebek, for setting a standard in my life as colleagues and friends. A special thanks to Andrew D. Hershberger, Kumiko Sano, Fang Zhou, and Justin Lee for their significant contribution as undergraduate researchers. Dr. John Erhlinger and Prof. Gregory S. Lee, thank you both for the enlight- ening discussions regarding statistical analysis, which have been put into use in this dissertation. Thank you, Elizabethanne Fuller-Murray, for always treating me like I was the most important student ever to walk into your office. To my wife, thank you for being my help in every way through this season. And thank you, my parents,