LEADER 06130nam 22006495 450 001 9910299564303321 005 20200630222149.0 010 $a981-10-6692-2 024 7 $a10.1007/978-981-10-6692-4 035 $a(CKB)4100000000881599 035 $a(DE-He213)978-981-10-6692-4 035 $a(MiAaPQ)EBC6299350 035 $a(MiAaPQ)EBC5591606 035 $a(Au-PeEL)EBL5591606 035 $a(OCoLC)1066188494 035 $a(PPN)220125066 035 $a(EXLCZ)994100000000881599 100 $a20171026d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKinesthetic Perception $eA Machine Learning Approach /$fby Subhasis Chaudhuri, Amit Bhardwaj 205 $a1st ed. 2018. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2018. 215 $a1 online resource (XV, 138 p. 50 illus., 44 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v748 311 $a981-10-6691-4 327 $aIntro -- Preface -- Acknowledgements -- Contents -- About the Authors -- 1 Introduction -- 1.1 Basics of Haptics -- 1.1.1 Various Research Areas in Haptics -- 1.1.2 Possible Applications -- 1.2 Kinesthetic Perception -- 1.3 Perception: Aware Engineering Design -- 1.4 Organization of the Book -- References -- 2 Perceptual Deadzone -- 2.1 Haptic Data Compression -- 2.2 Perceptual Deadzone for Multidimensional Signals -- 2.3 Effect of Rate of Change of Kinesthetic Stimuli -- References -- 3 Predictive Sampler Design for Haptic Signals -- 3.1 Introduction -- 3.2 Experimental Setup -- 3.2.1 Device Setup -- 3.2.2 Signal Characteristics -- 3.2.3 Lag in User Response -- 3.2.4 Collected Data -- 3.3 Classification of Haptic Response -- 3.3.1 Performance Metric -- 3.3.2 Weber Classifier -- 3.3.3 Level Crossing Classifier -- 3.3.4 Classifiers Based on Decision Tree and Random Forests -- 3.3.5 Effect of Temporal Spacing -- 3.3.6 Significance Test for Classifiers -- 3.4 Applications in Adaptive Sampling -- References -- 4 Deadzone Analysis of 2-D Kinesthetic Perception -- 4.1 Introduction -- 4.2 Experimental Setup -- 4.2.1 Signal Characteristics and User Response -- 4.2.2 Data Statistics -- 4.3 Determination of Perceptual Deadzone -- 4.3.1 The Weber Classifier -- 4.3.2 Level Crossing Classifier -- 4.3.3 Elliptical Deadzone -- 4.3.4 Oriented Elliptical Deadzone -- References -- 5 Effect of Rate of Change of Stimulus -- 5.1 Introduction -- 5.2 Design of Experiment -- 5.2.1 Kinesthetic Force Stimulus -- 5.2.2 Data Collection -- 5.3 System Correction -- 5.4 Estimation of Decision Boundary -- 5.4.1 Parametric Decision Boundary -- 5.4.2 Nonparametric Decision Boundary -- 5.5 Analysis of Results -- References -- 6 Temporal Resolvability of Stimulus -- 6.1 Introduction -- 6.1.1 Motivation for the Study -- 6.1.2 Related Work -- 6.1.3 Our Approach. 327 $a6.2 Experimental Setup -- 6.2.1 Signal Characteristics -- 6.2.2 Data Collection -- 6.3 Estimation of Temporal Resolution -- 6.4 Effect of Fatigue -- 6.5 Application in Data Communication -- References -- 7 Task Dependence of Perceptual Deadzone -- 7.1 Introduction -- 7.1.1 Objective of the Study -- 7.1.2 Prior Work -- 7.1.3 Our Approach -- 7.2 Design of Experiment -- 7.2.1 Kinesthetic Force Stimulus -- 7.2.2 Data Statistics -- 7.3 Estimation of Perceptual Deadzones -- References -- 8 Sequential Effect on Kinesthetic Perception -- 8.1 Introduction -- 8.2 Sequential Effect -- 8.3 Quantification of Sequential Effect -- 8.3.1 Logistic Regression -- 8.3.2 Description of the Regression Model -- 8.4 Analysis of Effect on Comparative Task -- 8.5 Analysis of Effect on Discriminative Task -- References -- 9 Conclusions -- Index. 330 $aThis book focuses on the study of possible adaptive sampling mechanisms for haptic data compression aimed at applications like tele-operations and tele-surgery. Demonstrating that the selection of the perceptual dead zones is a non-trivial problem, it presents an exposition of various issues that researchers must consider while designing compression algorithms based on just noticeable difference (JND). The book begins by identifying perceptually adaptive sampling strategies for 1-D haptic signals, and goes on to extend the findings on multidimensional signals to study directional sensitivity, if any. The book also discusses the effect of the rate of change of kinesthetic stimuli on the JND, temporal resolution for the perceivability of kinesthetic force stimuli, dependence of kinesthetic perception on the task being performed, the sequential effect on kinesthetic perception, and, correspondingly, on the perceptual dead zone. Offering a valuable resource for researchers, professionals, and graduate students working on haptics and machine perception studies, the book can also support interdisciplinary work focused on automation in surgery. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v748 606 $aRobotics 606 $aAutomation 606 $aArtificial intelligence 606 $aControl engineering 606 $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 615 0$aRobotics. 615 0$aAutomation. 615 0$aArtificial intelligence. 615 0$aControl engineering. 615 14$aRobotics and Automation. 615 24$aArtificial Intelligence. 615 24$aControl and Systems Theory. 676 $a004.77 700 $aChaudhuri$b Subhasis$4aut$4http://id.loc.gov/vocabulary/relators/aut$0846530 702 $aBhardwaj$b Amit$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299564303321 996 $aKinesthetic Perception$92504035 997 $aUNINA