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Design and Analysis of Predictive Sampling of Haptic Signals

Published: 08 December 2014 Publication History

Abstract

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.

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  • (2022)Perceptual Deadband for Haptic Data Compression: Symmetric or Asymmetric?2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN53752.2022.9900602(1258-1263)Online publication date: 29-Aug-2022
  • (2022)QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G EraIEEE Access10.1109/ACCESS.2022.319790010(85720-85754)Online publication date: 2022
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Published In

cover image ACM Transactions on Applied Perception
ACM Transactions on Applied Perception  Volume 11, Issue 4
January 2015
132 pages
ISSN:1544-3558
EISSN:1544-3965
DOI:10.1145/2695584
Issue’s Table of Contents
© 2014 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2014
Accepted: 01 May 2014
Revised: 01 May 2014
Received: 01 June 2013
Published in TAP Volume 11, Issue 4

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Author Tags

  1. Adaptive sampling
  2. Weber’s law
  3. decision tree
  4. level crossings
  5. linear regression
  6. random forest
  7. rate-distortion curve

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  • (2023)Limitations of the Perceptual Deadband Approach for Haptic Data Compression2023 National Conference on Communications (NCC)10.1109/NCC56989.2023.10067964(1-5)Online publication date: 23-Feb-2023
  • (2022)Perceptual Deadband for Haptic Data Compression: Symmetric or Asymmetric?2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN53752.2022.9900602(1258-1263)Online publication date: 29-Aug-2022
  • (2022)QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G EraIEEE Access10.1109/ACCESS.2022.319790010(85720-85754)Online publication date: 2022
  • (2021)A Network-Adaptive Prediction Algorithm for Haptic Data Under Network ImpairmentsIEEE Access10.1109/ACCESS.2021.30700639(52672-52683)Online publication date: 2021
  • (2019)Haptic Networking Supporting Vertical IndustriesEnabling 5G Communication Systems to Support Vertical Industries10.1002/9781119515579.ch3(41-73)Online publication date: 22-Jun-2019
  • (2018)Toward Haptic Communications Over the 5G Tactile InternetIEEE Communications Surveys & Tutorials10.1109/COMST.2018.285145220:4(3034-3059)Online publication date: Dec-2019
  • (2015)Estimation of resolvability of user response in kinesthetic perception of jump discontinuities2015 IEEE World Haptics Conference (WHC)10.1109/WHC.2015.7177758(482-487)Online publication date: Jun-2015
  • (2015)HoIP: A point-to-point haptic data communication protocol and its evaluation2015 Twenty First National Conference on Communications (NCC)10.1109/NCC.2015.7084908(1-6)Online publication date: Feb-2015

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