• DocumentCode
    165343
  • Title

    Optimization of indoor FES-cycling exercise assisted by a flywheel mechanism using genetic algorithm

  • Author

    Abdulla, S.C. ; Tokhi, M.O.

  • Author_Institution
    Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
  • fYear
    2014
  • fDate
    8-10 Oct. 2014
  • Firstpage
    1867
  • Lastpage
    1871
  • Abstract
    This paper presents an approach of parameter optimization of fuzzy logic control (FLC), assist mechanism and stimulation phases of functional electrical stimulation (FES)-cycling using genetic algorithm. The cycling exercise is introduced for rehabilitation of lower limbs by stimulating the quadriceps muscle of each leg by FES with the aid of a flywheel and electrical clutch assist mechanism. FLC is used to regulate the stimulation intensity on the muscles. Also, FLC is used to control the engagement of the flywheel, with the crank of the bicycle, to provide the required assistance. Genetic algorithm is used to optimize the FES-cycling with the objective function of minimizing the mean square error in cadence. In view of good results, it is concluded that the GA is effective in optimizing the performance of FES-cycling. Also, with the assistance of a flywheel and electrical clutch mechanism, it is possible to obtain cycling cadence close to the desired by stimulating single muscle only.
  • Keywords
    flywheels; fuzzy control; genetic algorithms; patient rehabilitation; FLC; crank; electrical clutch assist mechanism; flywheel mechanism; functional electrical stimulation; fuzzy logic control; genetic algorithm; indoor FES-cycling exercise; parameter optimization; Flywheels; Genetic algorithms; Muscles; Neuromuscular stimulation; Optimization; Sociology; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control (ISIC), 2014 IEEE International Symposium on
  • Conference_Location
    Juan Les Pins
  • Type

    conf

  • DOI
    10.1109/ISIC.2014.6967635
  • Filename
    6967635