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A Knowledge Based System for the Selection of Muscles for Gait Phase Detection using EMGs

Author

Listed:
  • Vasileios Syrimpeis

    (University of Patras, Department of Mechanical Engineering and Aeronautics, Patras, Greece)

  • Vassilis Moulianitis

    (University of the Aegean, Department of Product and System Design Engineering, Ermoupolis, Greece)

  • Nikos A. Aspragathos

    (University of Patras, Department of Mechanical Engineering and Aeronautics, Patras, Greece)

  • Elias Panagiotopoulos

    (University of Patras, Department of Orthopedic Surgery, Patras, Greece)

Abstract

Purpose: This paper presents the development of a knowledge based system for the detection of gait phases based on EMGs from muscles of the lower limb. Methods: An empirical analysis of the EMG characteristics for the most representative muscle of every muscle group concerning their suitability for the gait phase detection is presented. The same approach is applied to every lower limb muscle where an EMG could be received. The entities and the decision-making mechanism of the knowledge based system is presented in a formal way. Results: A knowledge based system is built upon the knowledge acquired from this analysis. Finally, an example is presented where the developed knowledge based system is used to support the conceptual design of a drop foot correction system. Conclusions: The knowledge based system can be used in the conceptual design of any rehabilitation system for lower limb disabilities using EMG signals from the lower limbs.

Suggested Citation

  • Vasileios Syrimpeis & Vassilis Moulianitis & Nikos A. Aspragathos & Elias Panagiotopoulos, 2017. "A Knowledge Based System for the Selection of Muscles for Gait Phase Detection using EMGs," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 12(2), pages 18-45, April.
    • Handle: RePEc:igg:jhisi0:v:12:y:2017:i:2:p:18-45
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