Data-Driven Playback of Natural Tactile Texture Via Broadband Friction Modulation

Roman V. Grigorii*, Roberta L. Klatzky, J. Edward Colgate

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We used broadband electroadhesion to reproduce the friction force profile measured as a finger slid across a textured surface. In doing so, we were also able to reproduce with high fidelity the skin vibrations characteristic of that texture; however, we found that this did not reproduce the original perception. To begin, the reproduction felt weak. In order to maximize perceptual similarity between a real texture and its friction force playback, the vibratory magnitude of the latter must be scaled up on average ≈ 3X for fine texture and ≈ 5X for coarse texture samples. This additional gain appears to correlate with perceived texture roughness. Additionally, even with optimal scaling and high fidelity playback, subjects could identify which of two reproductions corresponds to a real texture with only 71% accuracy, as compared to 95% accuracy when using real texture alternatives. We conclude that while tribometry and vibrometry data can be useful for texture classification, they appear to contribute only partially to texture perception. We propose that spatially distributed excitation of skin within the fingerpad may play an additional key role, and may thus be able to contribute to high fidelity texture reproduction.

Original languageEnglish (US)
Pages (from-to)429-440
Number of pages12
JournalIEEE Transactions on Haptics
Volume15
Issue number2
DOIs
StatePublished - 2022

Keywords

  • Electroadhesion
  • Surface haptics
  • Texture capture
  • Texture playback

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications

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