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Related Concept Videos

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
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Design Example: Resistive Touchscreen01:14

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A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
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Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
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A Tactile Automated Passive-Finger Stimulator TAPS
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A dataset for tactile textures on uneven surfaces collected using a BioIn-Tacto sensing module.

Maliheh Marzani1, Soheil Khatibi1, Ruslan Masinjila2

  • 1Department of Computer Science, Lakehead University, Orillia, ON, Canada.

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Area of Science:

  • Robotics
  • Materials Science
  • Sensor Technology

Background:

  • Human-like robotic manipulation requires effective texture recognition in varied environments.
  • Robots with tactile sensors must identify textures using touch-related features in unpredictable settings.
  • Advancing texture classification necessitates a comprehensive dataset of physical interactions between robotic probes and textures.

Purpose of the Study:

  • To create a novel dataset from signals collected by a bioinspired multimodal tactile sensing module.
  • To capture dynamic interactions between a robotic probe and 12 distinct tactile textures.
  • To support research in robotic texture classification and manipulation.

Main Methods:

  • A bioinspired multimodal tactile sensing module was employed.
  • Signals including pressure, acceleration, angular rate, and magnetic field variations were recorded at 130 Hz.
  • Each of the 12 textures was explored 25 times using a sliding motion, resulting in 300 total exploratory episodes.

Main Results:

  • A comprehensive dataset of tactile texture interactions was generated.
  • The dataset includes multi-sensor signals capturing physical contact dynamics.
  • The data encompasses 300 exploratory episodes across 12 different textures.

Conclusions:

  • The created tactile texture dataset is valuable for object recognition and robotic manipulation.
  • This dataset facilitates tactile texture reconstruction and recognition tasks.
  • It provides opportunities to study time series properties in robotic tactile exploration.