Haptic Material Analysis and Classification Inspired by Human Exploratory Procedures

Matti Strese, Lara Brudermueller, Jonas Kirsch, Eckehard Steinbach

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


We present a framework for the acquisition and parametrization of object material properties. The introduced acquisition device, denoted as Texplorer2, is able to extract surface material properties while a human operator is performing exploratory procedures. Using the Texplorer2, we scanned 184 material classes which we labeled according to biological, chemical, and geological naming conventions. Based on these real material recordings, we introduce a novel set of mathematical features which align with corresponding material properties defined in perceptual studies from related work and classify the materials using common machine learning techniques. Validation results of the proposed multi-modal features lead to an overall classification accuracy of 90.2% \pm 1.2% and an F_\text{\text{1}} score of 0.90 \pm 0.01 using the random forest classifier. For the sake of comparison, a deep neural network is trained and tested on images of the material surfaces; it outperforms (90.7% \pm 1.0%) the hand-crafted feature-based approach yet leads to more critical misclassifications in terms of the proposed taxonomy.

Original languageEnglish
Article number8894510
Pages (from-to)404-424
Number of pages21
JournalIEEE Transactions on Haptics
Issue number2
StatePublished - 1 Apr 2020


  • Surface Haptics
  • content-based features
  • material scanning


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