Research Article
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A Novel Fuzzy Logic Based Hand Gesture Recognition System

Year 2025, Volume: 13 Issue: 1, 76 - 83, 30.03.2025

Harun Sümbül

https://doi.org/10.17694/bajece.1519693

Abstract

In this proposed study, a Fuzzy Logic System (FLS) was developed to classify and detect hand movements. The designed FLS system consists of a Fuzzifier, Inference Engine, Knowledge Base, and Defuzzifier. The Mamdani technique was used as the Inference Engine, and the centroid method was used for Defuzzification. Five input variables (Flex1-5) and one output variable (Sign) were used to create a rule base with 94 rules. A sensor array was placed on a glove to generate data, and a data collection circuit was established. Movements were performed through this circuit to create the rule bases. A total of 15.030 data points were analyzed to develop the FLS. According to the results, the movements (97.5%) were detected successfully.

Keywords

Flex sensor fuzzy logic hand gesture recognition

Ethical Statement

All procedures carried out in studies involving human participants adhered to the ethical standards set by the institutional and national research committee. The study also conformed to the principles outlined in the 1964 Helsinki Declaration and its subsequent amendments or comparable ethical standards.

Project Number

PYO.YMY.1908.23.001

Thanks

The research presented in this study was supported by the Coordinatorship of Scientific Research Projects at Ondokuz Mayis University in Samsun, Turkey. The project was identified by the project number PYO.YMY.1908.23.001.

References

  • [1] Y. Zhang et al., “Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors”, Medical Engineering and Physics 124 (2024) 104060.
  • [2] M.A. Rahaman, M.H. Ali, and M. Hasanuzzaman, “Real-time computer vision-based gestures recognition system for Bangla sign language using multiple linguistic features analysis”, Multimedia Tools and Applications, 83, 22261–22294 (2024). https://doi.org/10.1007/s11042-023-15583-8.
  • [3] World Health Organization, “Deafness and hearing loss”, https://www.who.int/es/news-room/fact-sheets/detail/deafness-and-hearing-loss, 2021, Online; Accessed July 19.
  • [4] H. Yakut, “İşaret dili harflerinin görüntü işleme yöntemleriyle tanınması için bir uygulama”, Yüksek Lisans Tezi, Fırat Üniversitesi, Ocak 2013.
  • [5] F. ShengChen, C. MingFu, and C. LinHuang, “Hand gesture recognition using a real-time tracking method and hidden Markov models”, Image and Vision Computing, 21(8), 1 August 2003, Pages 745-758.
  • [6] D. Wu, L. Pigou, P.J. Kindermans, N.D.H. Le, L. Shao, J. Dambre, and J.M. Odobez, “Deep dynamic neural networks for multimodal gesture segmentation and recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8), 1583-1597, 2016.
  • [7] O. Güler, & I. Yücedağ, “Derin öğrenme ile el hareketi tanıma üzerine yapılan çalışmaların incelenmesi”, 21. Akademik Bilişim Konferansı, 2019, Ordu/Turkey.
  • [8] G. Devineau, F. Moutarde, W. Xi, & J. Yang, “Deep Learning for Hand Gesture Recognition on Skeletal Data”, In Automatic Face & Gesture Recognition (FG 2018), 13th IEEE International Conference on (pp. 106-113), IEEE, 2018.
  • [9] F. Başçiftçi, H. Sümbül, “Designing an expert system for detection of tuberculosis disease with logic simplification method”, e-Journal of New World Sciences Academy, NWSA Engineering Sciences, 1A0097, ISSN: 1308-7231, 5(3), 463-471, 2010.
  • [10] B. Ahmet, K. Yavuz, and K. Adem, “Bulanık uzman sistem yaklaşımı ile yeşil kart başvuru değerlendirme sistemi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 24, Sayı: 1, 63-76, 2010.
  • [11] H. Sümbül, A.H. Yüzer, “Design of a fuzzy input expert system visual information interface for classification of apnea and hypopnea”, Multimedia Tools and Applications, 83(7), 21133-21152, 2024. https://dx.doi.org/10.1007/s11042-023-16152-9.
  • [12] A.B. Sargano, X. Gu, P. Angelov, et al., “Human action recognition using deep rule-based classifier”, Multimedia Tools and Applications, 2020, 30653–30667. https://doi.org/10.1007/s11042-020-09381-9.
  • [13] B. Choi, S. Kang, K. Jun, et al., “Rule-based soft computing for edge detection”, Multimedia Tools and Applications, 24819–24831, 2017. https://doi.org/10.1007/s11042-016-4329-7.
  • [14] W. Bi, F. Gao, A. Zhang, et al., “A framework for extended belief rule base reduction and training with the greedy strategy and parameter learning”, Multimedia Tools and Applications, 11127–11143, 2022. https://doi.org/10.1007/s11042-022-12232-4.
  • [15] A. Biswas, A. Adan, P. Haldar, D. Majumder, V. Natale, C. Randler, L. Tonetti, and S. Sahu, “Exploration of transcultural properties of the reduced version of the Morningness–Eveningness Questionnaire (rMEQ) using adaptive neuro-fuzzy inference system”, Biological Rhythm Research, 45(6), 955-968, 2014. DOI: 10.1080/09291016.2014.939442.
  • [16] E. Allam, H.F. Elbab, M.A. Hady, and S. Abouel-Seoud, “Vibration control of active vehicle suspension system using fuzzy logic algorithm”, Fuzzy Information and Engineering, 2(4), 361-387, 2010. https://doi.org/10.1007/s12543-010-0056-3.
  • [17] Ö.M. Soysal, S. Shirzad, and K. Sekeroglu, “Facial action unit recognition using data mining integrated deep learning”, 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2017, pp. 437-443. doi: 10.1109/CSCI.2017.74.

Year 2025, Volume: 13 Issue: 1, 76 - 83, 30.03.2025

Harun Sümbül

https://doi.org/10.17694/bajece.1519693

Abstract

Project Number

PYO.YMY.1908.23.001

References

  • [1] Y. Zhang et al., “Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors”, Medical Engineering and Physics 124 (2024) 104060.
  • [2] M.A. Rahaman, M.H. Ali, and M. Hasanuzzaman, “Real-time computer vision-based gestures recognition system for Bangla sign language using multiple linguistic features analysis”, Multimedia Tools and Applications, 83, 22261–22294 (2024). https://doi.org/10.1007/s11042-023-15583-8.
  • [3] World Health Organization, “Deafness and hearing loss”, https://www.who.int/es/news-room/fact-sheets/detail/deafness-and-hearing-loss, 2021, Online; Accessed July 19.
  • [4] H. Yakut, “İşaret dili harflerinin görüntü işleme yöntemleriyle tanınması için bir uygulama”, Yüksek Lisans Tezi, Fırat Üniversitesi, Ocak 2013.
  • [5] F. ShengChen, C. MingFu, and C. LinHuang, “Hand gesture recognition using a real-time tracking method and hidden Markov models”, Image and Vision Computing, 21(8), 1 August 2003, Pages 745-758.
  • [6] D. Wu, L. Pigou, P.J. Kindermans, N.D.H. Le, L. Shao, J. Dambre, and J.M. Odobez, “Deep dynamic neural networks for multimodal gesture segmentation and recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8), 1583-1597, 2016.
  • [7] O. Güler, & I. Yücedağ, “Derin öğrenme ile el hareketi tanıma üzerine yapılan çalışmaların incelenmesi”, 21. Akademik Bilişim Konferansı, 2019, Ordu/Turkey.
  • [8] G. Devineau, F. Moutarde, W. Xi, & J. Yang, “Deep Learning for Hand Gesture Recognition on Skeletal Data”, In Automatic Face & Gesture Recognition (FG 2018), 13th IEEE International Conference on (pp. 106-113), IEEE, 2018.
  • [9] F. Başçiftçi, H. Sümbül, “Designing an expert system for detection of tuberculosis disease with logic simplification method”, e-Journal of New World Sciences Academy, NWSA Engineering Sciences, 1A0097, ISSN: 1308-7231, 5(3), 463-471, 2010.
  • [10] B. Ahmet, K. Yavuz, and K. Adem, “Bulanık uzman sistem yaklaşımı ile yeşil kart başvuru değerlendirme sistemi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt: 24, Sayı: 1, 63-76, 2010.
  • [11] H. Sümbül, A.H. Yüzer, “Design of a fuzzy input expert system visual information interface for classification of apnea and hypopnea”, Multimedia Tools and Applications, 83(7), 21133-21152, 2024. https://dx.doi.org/10.1007/s11042-023-16152-9.
  • [12] A.B. Sargano, X. Gu, P. Angelov, et al., “Human action recognition using deep rule-based classifier”, Multimedia Tools and Applications, 2020, 30653–30667. https://doi.org/10.1007/s11042-020-09381-9.
  • [13] B. Choi, S. Kang, K. Jun, et al., “Rule-based soft computing for edge detection”, Multimedia Tools and Applications, 24819–24831, 2017. https://doi.org/10.1007/s11042-016-4329-7.
  • [14] W. Bi, F. Gao, A. Zhang, et al., “A framework for extended belief rule base reduction and training with the greedy strategy and parameter learning”, Multimedia Tools and Applications, 11127–11143, 2022. https://doi.org/10.1007/s11042-022-12232-4.
  • [15] A. Biswas, A. Adan, P. Haldar, D. Majumder, V. Natale, C. Randler, L. Tonetti, and S. Sahu, “Exploration of transcultural properties of the reduced version of the Morningness–Eveningness Questionnaire (rMEQ) using adaptive neuro-fuzzy inference system”, Biological Rhythm Research, 45(6), 955-968, 2014. DOI: 10.1080/09291016.2014.939442.
  • [16] E. Allam, H.F. Elbab, M.A. Hady, and S. Abouel-Seoud, “Vibration control of active vehicle suspension system using fuzzy logic algorithm”, Fuzzy Information and Engineering, 2(4), 361-387, 2010. https://doi.org/10.1007/s12543-010-0056-3.
  • [17] Ö.M. Soysal, S. Shirzad, and K. Sekeroglu, “Facial action unit recognition using data mining integrated deep learning”, 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2017, pp. 437-443. doi: 10.1109/CSCI.2017.74.
There are 17 citations in total.

Details

Primary Language English
Subjects Bioengineering (Other), Electrical Engineering (Other)
Journal Section Araştırma Articlessi
Authors

Harun Sümbül 0000-0001-5135-3410

Project Number PYO.YMY.1908.23.001
Early Pub Date May 15, 2025
Publication Date March 30, 2025
Submission Date July 20, 2024
Acceptance Date October 2, 2024
Published in Issue Year 2025 Volume: 13 Issue: 1

Cite

APA Sümbül, H. (2025). A Novel Fuzzy Logic Based Hand Gesture Recognition System. Balkan Journal of Electrical and Computer Engineering, 13(1), 76-83. https://doi.org/10.17694/bajece.1519693
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