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Related Concept Videos

Tactile and Chemical Senses01:27

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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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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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A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition.

Simone Ciotti1,2, Edoardo Battaglia3, Nicola Carbonaro4

  • 1Centro di Ricerca "E. Piaggio", University of Pisa, Largo Lucio Lazzarino 1, Pisa 56126, Italy. simone.ciotti@ing.unipi.it.

Sensors (Basel, Switzerland)
|June 9, 2016
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Summary

This study developed an under-sensed glove using five sensors and hand synergy information for accurate hand pose reconstruction. This technology enables robust recognition of functional grasps with high precision.

Keywords:
human hand synergieskinematic wearable sensingoptimal designunder-sensing

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Robotics

Background:

  • Hand pose reconstruction (HPR) is challenging due to complex hand biomechanics.
  • Wearable sensors offer natural kinematic monitoring but face accuracy and cost limitations.
  • Existing HPR systems are often over-sensed, impacting ergonomics and cost.

Purpose of the Study:

  • To develop an optimally-designed, under-sensed glove for hand kinematics measurement.
  • To leverage hand synergy information for improved HPR accuracy with minimal sensors.
  • To enable robust functional grasp recognition using reconstructed hand poses.

Main Methods:

  • Combined optimal HPR device design principles with knitted piezoresistive fabrics (KPF) technology.
  • Developed an under-sensed glove with five optimally placed textile goniometers.
  • Utilized hand synergy information (inter-joint covariation) to reconstruct 19 degrees of freedom hand kinematics.
  • Implemented an unsupervised method for functional grasp recognition.

Main Results:

  • Achieved accurate and reliable kinematic hand pose reconstructions using only five sensors.
  • Demonstrated the effectiveness of hand synergy information in under-sensed HPR.
  • Successfully recognized eight functional grasps with high accuracy and robustness across five subjects.

Conclusions:

  • An optimally-designed, under-sensed glove using KPF technology can achieve accurate hand pose reconstruction.
  • Hand synergy information is crucial for effective HPR with limited wearable sensors.
  • The developed system shows promise for applications requiring robust and accurate grasp recognition.