Kinesthetic Perception : a Machine Learning Approach / by Subhasis Chaudhuri, Amit Bhardwaj.

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Kinesthetic Perception : a Machine Learning Approach / by Subhasis Chaudhuri, Amit Bhardwaj.

9789811066924
9811066922
9789811066917
9811066914

Singapore : Springer Singapore : Imprint : Springer, 2018.

English

1 online resource (xv, 138 pages) : illustrations.

10.1007/978-981-10-6692-4 doi

QA76.9.H85

629.892

This 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.

Includes bibliographical references and index.

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Studies in computational intelligence ; 748.

Print version: 9789811066917

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