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Design and Analysis of Predictive Sampling of Haptic Signals

DSpace at IIT Bombay

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Title Design and Analysis of Predictive Sampling of Haptic Signals
 
Creator BHARDWAJ, A
CHAUDHURI, S
DABEER, O
 
Subject TELEACTION SYSTEMS
TIME-DELAY
DATA REDUCTION
TELEPRESENCE
COMMUNICATION
TRANSMISSION
PERCEPTION
Experimentation
Human factors
Adaptive sampling
Weber's law
level crossings
linear regression
decision tree
random forest
rate-distortion curve
 
Description In this article, we identify adaptive sampling strategies for haptic signals. Our approach relies on experiments wherein we record the response of several users to haptic stimuli. We then learn different classifiers to predict the user response based on a variety of causal signal features. The classifiers that have good prediction accuracy serve as candidates to be used in adaptive sampling. We compare the resultant adaptive samplers based on their rate-distortion tradeoff using synthetic as well as natural data. For our experiments, we use a haptic device with a maximum force level of 3 N and 10 users. Each user is subjected to several piecewise constant haptic signals and is required to click a button whenever he perceives a change in the signal. For classification, we not only use classifiers based on level crossings and Weber's law but also random forests using a variety of causal signal features. The random forest typically yields the best prediction accuracy and a study of the importance of variables suggests that the level crossings and Weber's classifier features are most dominant. The classifiers based on level crossings and Weber's law have good accuracy (more than 90%) and are only marginally inferior to random forests. The level crossings classifier consistently outperforms the one based on Weber's law even though the gap is small. Given their simple parametric form, the level crossings and Weber's law-based classifiers are good candidates to be used for adaptive sampling. We study their rate-distortion performance and find that the level crossing sampler is superior. For example, for haptic signals obtained while exploring various rendered objects, for an average sampling rate of 10 samples per second, the level crossings adaptive sampler has a mean square error about 3dB less than the Weber sampler.
 
Publisher ASSOC COMPUTING MACHINERY
 
Date 2016-01-15T09:55:43Z
2016-01-15T09:55:43Z
2015
 
Type Article
 
Identifier ACM TRANSACTIONS ON APPLIED PERCEPTION, 11(4)
1544-3558
1544-3965
http://dx.doi.org/10.1145/2670533
http://dspace.library.iitb.ac.in/jspui/handle/100/18306
 
Language en