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    <documents>
      <document id='https://researchdata.bath.ac.uk/id/document/17637'>
        <docid>17637</docid>
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            <fileid>64659</fileid>
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            <filename>VibroTact_3D_printed_textures_data.zip</filename>
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            <mtime>2023-11-28 13:52:43</mtime>
            <url>https://researchdata.bath.ac.uk/1351/1/VibroTact_3D_printed_textures_data.zip</url>
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        <formatdesc>This files contains data collected with the VibroTact sensor from eight artificial textures with different roughness. The data is collected using five different sliding exploratory procedures.</formatdesc>
        <language>en</language>
        <security>public</security>
        <license>cc_by</license>
        <main>VibroTact_3D_printed_textures_data.zip</main>
        <content>data</content>
      </document>
    </documents>
    <eprint_status>archive</eprint_status>
    <userid>9738</userid>
    <dir>disk0/00/00/13/51</dir>
    <datestamp>2024-06-18 14:31:35</datestamp>
    <lastmod>2024-06-22 04:52:10</lastmod>
    <status_changed>2024-06-18 14:31:35</status_changed>
    <type>data_collection</type>
    <metadata_visibility>show</metadata_visibility>
    <creators>
      <item>
        <name>
          <family>Martinez Hernandez</family>
          <given>Uriel</given>
        </name>
        <id>U.Martinez@bath.ac.uk</id>
        <orcid>0000-0002-9922-7912</orcid>
        <affiliation>University of Bath</affiliation>
        <contact>TRUE</contact>
      </item>
      <item>
        <name>
          <family>Guo</family>
          <given>Quan</given>
        </name>
        <id>qg297@bath.ac.uk</id>
        <affiliation>University of Bath</affiliation>
        <contact>FALSE</contact>
      </item>
    </creators>
    <title>Dataset for &quot;VibroTact: Soft piezo vibration fingertip sensor for recognition of texture roughness via robotic sliding exploratory procedures&quot;</title>
    <subjects>
      <item>EA0090</item>
      <item>FB0010</item>
    </subjects>
    <divisions>
      <item>dept_elec_eng</item>
    </divisions>
    <keywords>Tactile sensing, texture roughness recognition, soft sensors, vibration data, machine learning, robotics</keywords>
    <abstract>This file contains data collected with the VibroTact sensor from eight artificial textures with different roughness.
The data is collected using five different sliding exploratory procedures.

The main folder is composed of eight folders where the folder name indicates the texture roughness as follows:
- Folder name: 2_5mm -&gt; texture roughness 2.5mm
- Folder name: 5mm -&gt; texture roughness 5.0mm
- Folder name: 7_5mm -&gt; texture roughness 7.5mm
- Folder name: 10mm -&gt; texture roughness 10.0mm
- Folder name: 12_5mm -&gt; texture roughness 12.5mm
- Folder name: 15mm -&gt; texture roughness 15.0mm
- Folder name: 17_5mm -&gt; texture roughness 17.5mm
- Folder name: 20mm -&gt; texture roughness 20.0mm

Each of these files contains five folders with the data collected from the artificial textures using five different types of sliding exploratory procedures. These sliding procedures are as follows:
- horizontal_sliding: the VibroTact sensor slides horizontally from left to right directions and backwards.
- vertical_sliding: the VibroTact sensor slides vertically up to down and backwards.
- left_diagonal_sliding: the VibroTact sensor slides from the bottom right corner of the texture to the top left corner and backwards.
- right_diagonal_sliding: the VibroTact sensor slides from the bottom left corner of the texture to the top right corner and backwards.
- square_sliding: the VibroTact sensor slides along the four sides of the textures.

Each of these folders contains CSV files (between 100 to 120) with the actual data from the four piezo vibration sensors (channel A, channel B, channel C, channel D) embedded in the soft fingertip of the VibroTact. This data can be used together with Machine Learning methods for exploration and recognition processes.</abstract>
    <date>2024-06-18</date>
    <publisher>University of Bath</publisher>
    <full_text_status>public</full_text_status>
    <corp_contributors>
      <item>
        <type>RightsHolder</type>
        <corpname>University of Bath</corpname>
      </item>
    </corp_contributors>
    <funding>
      <item>
        <funder_name>Self-funded</funder_name>
      </item>
    </funding>
    <research_centres>
      <item>cent_aur</item>
      <item>cent_dmade</item>
    </research_centres>
    <collection_method>The methodology can be found in the associated paper.</collection_method>
    <language>en</language>
    <version>1</version>
    <doi>10.15125/BATH-01351</doi>
    <related_resources>
      <item>
        <link>https://doi.org/10.1109/LSENS.2024.3409670</link>
        <type>pub</type>
      </item>
    </related_resources>
    <access_types>
      <item>open</item>
    </access_types>
    <resourcetype>
      <general>Dataset</general>
    </resourcetype>
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