GMRV Publications

Improving the Discriminability of Haptic Icons: The Haptic Tuning Fork

Laura Raya, Sara A.Boga, Marcos J. García-Lorenzo, Sofia Bayona
Applied Science, Volume 11, Number 18, page 8772 - September 2021
Download the publication : applsci-11-08772-v2.pdf [604Ko]  
Technological advances enable the capture and management of complex data sets that need to be correctly understood. Visualisation techniques can help in complex data analysis and exploration, but sometimes the visual channel is not enough, or it is not always available. Some authors propose using the haptic channel to reinforce or substitute the visual sense, but the limited human haptic short-term memory still poses a challenge. We present the haptic tuning fork, a reference signal displayed before the haptic information for increasing the discriminability of haptic icons. With this reference, the user does not depend only on short-term memory. We have decided to evaluate the usefulness of the haptic tuning fork in impedance kinesthetic devices as these are the most common. Furthermore, since the renderable signal ranges are device-dependent, we introduce a methodology to select a discriminable set of signals called the haptic scale. Both the haptic tuning fork and the haptic scale proved their usefulness in the performed experiments regarding haptic stimuli varying in frequency

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BibTex references

@Article\{RAGB21,
  author       = "Raya, Laura and A.Boga, Sara and García-Lorenzo, Marcos J. and Bayona, Sofia",
  title        = "Improving the Discriminability of Haptic Icons: The Haptic Tuning Fork",
  journal      = "Applied Science",
  number       = "18",
  volume       = "11",
  pages        = "8772",
  month        = "September",
  year         = "2021",
  note         = "https://doi.org/10.3390/app11188772",
  url          = "http://gmrv.es/Publications/2021/RAGB21"
}

Other publications in the database

» Laura Raya
» Marcos J. García-Lorenzo
» Sofia Bayona