The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis.

Journal: Expert review of medical devices
Published Date: Dec 7, 2020

Abstract

: Although there is a need for rehabilitation treatment with the increase in the aging population, the shortage of skilled physicians frustrates this necessity. Robotic technology has been advocated as one of the most viable methods with the potential to replace humans in providing physical rehabilitation of patients with neurological impairment. However, because the pioneering robot devices suffer several reservations such as safety and comfort concerns in clinical practice, there is an urgent need to provide upgraded replacements. The rapid development of intelligent computing has attracted the attention of researchers concerning the utilization of computational intelligence algorithms for robots in rehabilitation. : This article reviews the state of the art and advances of robotic neurorehabilitation with computational intelligence. We classified advances into two categories: mechanical structures and control methods. Prospective outlooks of rehabilitation robots also have been discussed. : The aggravation of global aging has promoted the application of robotic technology in neurorehabilitation. However, this approach is not mature enough to guarantee the safety of patients. Our critical review summarizes multiple computation algorithms which have been proved to be valuable for better robotic use in clinical settings and guide the possible future advances in this industry.

Authors

  • Jiali Yang
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China.
  • Zhiqi Zhao
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China.
  • Chenzhen Du
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Qin Peng
    Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, San Diego, CA, United States.
  • Juhui Qiu
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China.
  • Guixue Wang
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China.

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