PCA and deep learning based myoelectric grasping control of a prosthetic hand.

Journal: Biomedical engineering online
Published Date: Aug 6, 2018

Abstract

BACKGROUND: For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient's sense of using prosthetic hand and the thus improving the quality of life.

Authors

  • Chuanjiang Li
    The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 201418, China. licj@shnu.edu.cn.
  • Jian Ren
    State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China. Electronic address: renjian@sysucc.org.cn.
  • Huaiqi Huang
    BME Lab, Institute for Human Centered Engineering, Bern University of Applied Sciences, Quellgasse 21, 2502, Biel, Switzerland. huaiqi.huang@epfl.ch.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Yanfei Zhu
    The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 201418, China.
  • Huosheng Hu
    School of Computer Science & Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK.

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