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GenVibe: Exploration of Interactive Generation of Personal Vibrotactile Patterns

Published: 06 June 2020 Publication History

Abstract

Research about vibrotactile patterns is traditionally conducted with patterns handcrafted by experts which are then subsequently evaluated in general user studies. The current empirical approach to designing vibrotactile patterns mostly utilizes expert decisions and is notably not adapted to individual differences in the perception of vibration. This work describes GenVibe: a novel approach to designing vibrotactile patterns by examining the automatic generation of personal patterns. GenVibe adjusts patterns to the perception of an individual through the utilization of interactive generative models. An algorithm is described and tested with a dummy smartphone made from off-the-shelf electronic components. Afterward, a user study with 11 participants evaluates the outcome of GenVibe. Results show a significant increase in accuracy from 73.6% to 84.0% and a higher confidence ratings by the users.

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Cited By

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  • (2022)Preference-Driven Texture Modeling Through Interactive Generation and SearchIEEE Transactions on Haptics10.1109/TOH.2022.317393515:3(508-520)Online publication date: 1-Jul-2022
  • (2020)bARefootProceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology10.1145/3379337.3415828(579-593)Online publication date: 20-Oct-2020

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cover image ACM Other conferences
AHs '20: Proceedings of the Augmented Humans International Conference
March 2020
296 pages
ISBN:9781450376037
DOI:10.1145/3384657
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2020

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Author Tags

  1. generative models
  2. genetic algorithm
  3. haptics
  4. vibrotactile patterns

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AHs '20
AHs '20: Augmented Humans International Conference
March 16 - 17, 2020
Kaiserslautern, Germany

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Cited By

View all
  • (2022)Preference-Driven Texture Modeling Through Interactive Generation and SearchIEEE Transactions on Haptics10.1109/TOH.2022.317393515:3(508-520)Online publication date: 1-Jul-2022
  • (2020)bARefootProceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology10.1145/3379337.3415828(579-593)Online publication date: 20-Oct-2020

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