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Somatosensation

Somatosensation

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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Related Experiment Video

Updated: Jul 4, 2026

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

Extracting textural features from tactile sensors.

J Edwards¹, J Lawry, J Rossiter

¹Bristol Robotics Laboratory, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK. james.edwards@brl.ac.uk

Bioinspiration & Biomimetics

|June 28, 2008

Summary

This summary is machine-generated.

This study quantifies texture by analyzing frictional sounds with an artificial finger. Principal Component Analysis (PCA) effectively classifies textures, outperforming simple numerical features for complex data analysis.

Related Experiment Videos

Last Updated: Jul 4, 2026

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

Area of Science:

Tribology
Biomimetics
Signal Processing

Background:

Quantifying surface texture is crucial in material science and engineering.
Traditional methods can be complex and time-consuming.
Biologically inspired approaches offer novel solutions for tactile sensing.

Purpose of the Study:

To develop and evaluate a novel method for texture quantification using frictional sound.
To compare the effectiveness of Principal Component Analysis (PCA) with traditional numerical features for texture classification.
To investigate the influence of environmental factors, such as temperature, on texture analysis.

Main Methods:

An artificial finger equipped with a microphone was used to record frictional sounds from artificial surfaces.
Fast Fourier Transforms (FFTs) were employed to analyze sound recordings in the frequency domain.
Numerical features (modal frequency, average value) and PCA were calculated and compared.
K-nearest neighbors and clustering algorithms were used for data classification and analysis.

Main Results:

PCA demonstrated superior performance in classifying diverse textures compared to numerical features.
Clustering PCA-transformed data resulted in accurate grouping of similar surfaces.
Numerical features struggled to classify multiple textural elements and led to erroneous clustering.
Artificial finger temperature was found to influence certain calculated features and PCA results.

Conclusions:

Frictional sound analysis, particularly when combined with PCA, offers a robust and accurate method for texture quantification.
This biomimetic approach provides a promising alternative to traditional texture analysis techniques.
Further research can explore the application of this method in various industrial and research settings.