Kinesthetic Perception

Prof. Subhasis Chaudhuri received his B.Tech. Degree in Electronics and Electrical Communication Engineering from the Indian Institute of Technology Kharagpur, Kharagpur in 1985. He received his M.Sc. and Ph.D. degrees, both in Electrical Engineering, from the University of Calgary, Canada, and the University of California, San Diego, respectively. He joined the Department of Electrical Engineering at the Indian Institute of Technology Bombay, Bombay in 1990 as an Assistant Professor and is currently serving as the KN Bajaj Chair Professor. He has also served as the Head of the Department, the Dean (International Relations) and a Deputy Director. He has also served as a Visiting Professor at the University of Erlangen-Nuremberg, the Technical University of Munich and the University of Paris XI. He is a Fellow of the science and engineering Academies in India. He is a recipient of the Dr. Vikram Sarabhai Research Award (2001), the Swarnajayanti Fellowship (2003), the S.S. BhatnagarPrize in engineering sciences (2004) and the J.C. Bose National Fellowship (2008). He is a co-author of the books ‘Depth from Defocus: A Real Aperture Imaging Approach’, ‘Motion-Free Super-Resolution’, and ‘Blind Image Deconvolution: Methods and Convergence’, all published by Springer, New York (NY). He is currently an associate editor for the journal International Journal of Computer Vision. His primary areas of research include image processing and computational haptics.Amit Bhardwaj received his B.Tech. and M.E. degrees in Electronics and Communication Engineering from the YMCA Institute of Engineering, Faridabad, Haryana, and the Delhi College of Engineering, Delhi, in 2009 and 2011, respectively. He has recently completed his Ph.D in Electrical Engineering at the Indian Institute of Technology Bombay, Bombay, and is currently an Alexander von Humboldt Fellow at the Technical University of Munich. His current research areas include signal processing, haptics, kinesthetic perception, haptic data communication and applications of machine learning.

• Introduction.- Perceptual compression.- Predictive sampler design.- Perceptual deadzone for vector valued stimulus.- Temporal resolvability  of kinesthetic stimuli.- Effect of rate of change of stimulus.- Dependence of deadzone on task specificity.- Sequential effect on kinesthetic perception.