LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor

Research output: Contribution to conferencePaperpeer-review

Abstract

Modelling human grasping and transferring this data to an anthropomorphic robotic hand to endow it with human like grasping capabilities is a complex task. In this paper the use of surface electromyography (sEMG) for classification of functional grasps associated with everyday life is carried out using a low-cost wearable sensor in conjunction with state-of-the-art recurrent neural networks. The results produced through these experiments demonstrate the potential for sEMG to be used as an effective medium for transferring human demonstration to a robotic system.
Original languageEnglish
Pages213-222
Number of pages10
DOIs
Publication statusPublished - 25 Jun 2021
Event7th Annual International Conference on Control, Automation and Robotics - Singapore
Duration: 23 Apr 202126 Apr 2021
http://iccar.org/index.html

Conference

Conference7th Annual International Conference on Control, Automation and Robotics
Abbreviated titleICCAR
Period23/04/2126/04/21
Internet address

Keywords

  • Recurrent Neural Network
  • Pattern Classification
  • Grasping
  • Muscles
  • Robot Sensing Systems
  • Task Analysis
  • Training

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