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Kinesthetic Perception - A Machine Learning Approach (Paperback, Softcover reprint of the original 1st ed. 2018)
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Kinesthetic Perception - A Machine Learning Approach (Paperback, Softcover reprint of the original 1st ed. 2018)
Series: Studies in Computational Intelligence, 748
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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.
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