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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Grasp to See-Object Classification Using Flexion Glove with Support Vector Machine.

Shun-Hsin Yu1, Jen-Shuo Chang2, Chia-Hung Dylan Tsai1,2

  • 1Graduate Degree Program of Robotics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel object classification system using a flexion glove and machine learning. The system accurately identifies objects based on grasp data, offering potential for assistive technologies.

Keywords:
flex sensinggraspingmachine learningobject classification

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Machine Learning and Pattern Recognition

Background:

  • Object recognition is crucial for assistive technologies, especially for individuals with visual impairments.
  • Current methods often rely on visual input, limiting functionality in low-light or no-vision environments.

Purpose of the Study:

  • To develop and evaluate an object classification method using a flexion glove and machine learning.
  • To assess the system's accuracy across diverse object sets, including custom-printed and everyday items.

Main Methods:

  • A custom flexion glove with five flex sensors was developed to capture finger flexion data during object grasping.
  • Grasping data was segmented into picking, holding, and releasing phases, with features extracted from the holding phase.
  • A support vector machine (SVM) classifier was trained using the extracted grasping features.

Main Results:

  • The system achieved 95.56% classification accuracy on a set of printed objects with defined shapes and sizes.
  • An accuracy of 88.89% was obtained on a set of nine randomly selected daily-life objects.
  • The developed flexion glove demonstrated successful object classification capabilities.

Conclusions:

  • The proposed flexion glove and machine learning approach offers a viable method for object classification.
  • This technology holds promise for grasp-to-see applications, including aids for the visually impaired and object recognition in dark environments.