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A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair

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Abstract

This paper presents the design and implementation of a low-cost solar-powered wheelchair for physically challenged people. The signals necessary to maneuver the wheelchair are acquired from different muscles of the hand using surface electromyography (sEMG) technique. The raw sEMG signals are collected from the upper limb muscles which are then processed, characterized, and classified to extract necessary features for the generation of control signals to be used for the automated movement of the wheelchair. An artificial neural network-based classifier is constructed to classify the patterns and features extracted from the raw sEMG signals. The classification accuracy of the extracted parameters from the sEMG signals is found to be relatively high in comparison with the existing methods. The extracted parameters used to generate control signals that are then fed into a microcomputer-based control system (MiCS). A solar-powered wheelchair prototype is developed, and the above MiCS is introduced to control its maneuver using the sEMG signals. The prototype is then thoroughly tested with sEMG signals from patients of different age groups. Also, the life cycle cost analysis of the proposed wheelchair revealed that it is financially feasible and cost-effective.

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Author’s Contribution

This work was carried out in close collaboration between all co-authors. MSK, ZIC, SAM, and MM first defined the research theme and contributed an early design of the system. MSK, ZIC, and SAM further implemented and refined the system development. MSK, ZIC, SAM, MM, and AH wrote the paper. All authors have contributed to, seen, and approved the final manuscript.

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Authors and Affiliations

  1. Institute of Information Technology, Jahangirnagar University, Savar, 1342, Dhaka, Bangladesh

    M. Shamim Kaiser, Zamshed Iqbal Chowdhury & Shamim Al Mamun

  2. University of Stirling, Stirling, FK9 4LA, Scotland, UK

    Amir Hussain

  3. Anhui University, Hefei, 230601, Anhui, China

    Amir Hussain

  4. NeuroChip Lab, Department of Biomedical Sciences, University of Padova, 35131, Padova, Italy

    Mufti Mahmud

  5. Theoretical Neurobiology and Neuroengineering, University of Antwerp, 2610, Wilrijk, Belgium

    Mufti Mahmud

  6. COSIPRA Lab, University of Stirling, Stirling, FK9 4LA, Scotland, UK

    Mufti Mahmud

Authors
  1. M. Shamim Kaiser
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  2. Zamshed Iqbal Chowdhury
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  3. Shamim Al Mamun
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  4. Amir Hussain
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  5. Mufti Mahmud
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Corresponding author

Correspondence to Mufti Mahmud.

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Conflict of Interest

M. Shamim Kaiser, Zamshed Iqbal Chowdhury, Shamim Al Mamun, Amir Hussain and Mufti Mahmud declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Informed Consent

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by the any of the authors.

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Kaiser, M.S., Chowdhury, Z.I., Mamun, S.A. et al. A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair. Cogn Comput 8, 946–954 (2016). https://doi.org/10.1007/s12559-016-9398-4

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  • DOI: https://doi.org/10.1007/s12559-016-9398-4

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