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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: Aug 5, 2025

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Published on: May 20, 2020

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand.

Moaed A Abd¹, Erik D Engeberg^1,2

¹Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, Florida, FL, USA.

Research Square

|March 30, 2023

Summary

This summary is machine-generated.

People with upper limb absence (ULA) can integrate multiple haptic feedback channels for dexterous prosthetic hand control. This study shows ULA individuals can multitask with advanced prosthetic hands, improving functionality for amputees.

Keywords:

Amputee Artificial Intelligence Haptic Feedback Neural Network Prosthetic Hand Sensorimotor Integration Slip prevention

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Published on: April 21, 2023

Area of Science:

Biomedical Engineering
Neuroscience
Rehabilitation Science

Background:

Prosthetic hands lack sophisticated haptic feedback and multitasking capabilities, unlike human hands.
Research is limited on integrating multichannel haptic feedback for upper limb absent (ULA) individuals' prosthetic control.
Existing prosthetic technology does not fully support the complex sensorimotor integration required for dexterous tasks.

Approach:

Developed a novel experimental paradigm for 12 subjects (3 ULA, 9 non-ULA) to test haptic feedback integration.
Utilized artificial neural networks (ANNs) for electromyogram signal pattern recognition and tactile sensor data classification.
Implemented vibrotactile actuators to provide context-specific haptic feedback encoding object slip direction at prosthetic fingertips.

Key Points:

Subjects achieved 95.53% accuracy in integrating two simultaneous haptic feedback channels for prosthetic hand control.
No significant difference in classification accuracy between ULA and non-ULA subjects.
ULA individuals required more time to respond, indicating a higher cognitive load for multichannel feedback integration.

Conclusions:

ULA individuals demonstrate the capacity to integrate nuanced, multichannel haptic feedback into their prosthetic hand control.
Findings represent a significant step towards enabling amputees to multitask with dexterous prosthetic hands.
Further research can advance prosthetic technology to better support complex sensorimotor integration and functional multitasking.