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Related Concept Videos

Tactile and Chemical Senses01:27

Tactile and Chemical Senses

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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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Somatosensation01:33

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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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Perceiving Loudness, Pitch, and Location01:21

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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Perception of Sound Waves01:01

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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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Mechanical vibrators are instrumental in compacting newly poured concrete within formwork and around reinforcements. This process is essential to eliminate trapped air pockets and establish a dense concrete mass. One widely used method is vibrating by internal vibrators, often referred to as a poker vibrator or immersion vibrator. It is rapidly inserted through the full depth of the freshly laid concrete and slightly extends into the layer below it (which remains in a plastic state). Consistent...
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Sensory Perception: Organization of the Somatosensory System01:11

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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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Related Experiment Video

Updated: Dec 13, 2025

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
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Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation.

Yerkebulan Massalim1, Zhanat Kappassov1, Huseyin Atakan Varol1

  • 1Department of Robotics and Mechatronics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.

Sensors (Basel, Switzerland)
|July 30, 2020
PubMed
Summary

This study introduces a Deep Learning (DL) method for fast object texture recognition and slip detection using tactile signals. Deep convolutional neural networks (CNNs) achieved high accuracy, enabling real-time robotic manipulation.

Keywords:
accelerometersconvolutional neural networksdeep learninglong short-term memoryslip detectiontactile sensingtexture identification

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Area of Science:

  • Robotics and Artificial Intelligence
  • Sensor Technology and Signal Processing

Background:

  • Autonomous dexterous manipulation requires robust object recognition and slip detection.
  • Dynamic tactile signals, particularly from vibrotactile sensors, are crucial for identifying objects and preventing slippage.
  • Existing methods often lack the speed and accuracy needed for real-time robotic applications.

Purpose of the Study:

  • To develop and evaluate a Deep Learning (DL) based method for simultaneous texture recognition and slip detection.
  • To achieve high accuracy and rapid processing for real-time robotic grasping and manipulation.
  • To compare the performance of different neural network architectures for tactile signal analysis.

Main Methods:

  • A Deep Learning (DL) approach was implemented for analyzing dynamic tactile signals.
  • The method simultaneously detects slip events, estimates velocity, and discriminates textures.
  • Comparative analysis involved Convolutional Neural Networks (CNNs), feed-forward neural networks, and Long Short-Term Memory (LSTM) networks, using accelerometers on an industrial gripper.

Main Results:

  • The DL-based method achieved simultaneous texture recognition and slip detection within 17 milliseconds.
  • Deep CNNs demonstrated superior generalization accuracy compared to other evaluated network types.
  • A signal bandwidth of 125 Hz was found sufficient for classifying textures with 80% accuracy.

Conclusions:

  • Deep CNNs offer a highly effective solution for real-time tactile-based object recognition and slip detection in robotics.
  • The developed method significantly enhances the capabilities of autonomous dexterous manipulation.
  • Optimized signal processing parameters, like bandwidth, can maintain high performance while reducing computational load.