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Fibers and Polymers

, Volume 19, Issue 12, pp 2657–2666 | Cite as

Human Motion Recognition Using E-textile Sensor and Adaptive Neuro-Fuzzy Inference System

  • Chicuong Vu
  • Jooyong KimEmail author
Article
  • 8 Downloads

Abstract

The present paper is intended to introduce a new approach in order to classify human body movements by using textile sensor embedded fabrics. An intelligent processing model embedded in muscle activity pants has been developed based on adaptive neuro-fuzzy inference System (ANFIS) in order to recognize the types of several standard human motions. The processing circuit would digitize motion data from the fabric stretch sensor developed in previous research. Data were continuously flowed into the memory of microcontroller chip and processed in order to get important factors like as input variables of the classification model. The parameters chosen for developing the ANFIS system are the average of amplitude (AMP), the standard deviation of amplitude (STD), and the average cycle (CYC). The final decision on the types of the motions would be stored or transmitted to nearby monitoring devices. In this study, laboratory scale experiments were conducted for four different types of human motions such as walking, jumping, running, and sprinting in order to examine the feasibility of the ANFIS model developed. The accuracy of ANFIS model was compared with results of fuzzy inference system (FIS) model and artificial neural network (ANN) model. As expected, the results indicated that the adaptive neurofuzzy expert system developed could be used as one of the smart simulators in order to recognize human motions with robust and high accuracy classification rate. Based on the test statistics, ANFIS model has been proved to be superior to ANN and FIS in terms of classification rate.

Keywords

Adaptive neuro-fuzzy inference system (ANFIS) E-textile sensor Wearable device Human motion classification 

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References

  1. 1.
    V. Custodio, F. J. Herrera, G. Lopez, and J. I. Moreno, Sensors, 12, 13907 (2012).CrossRefGoogle Scholar
  2. 2.
    O. D. Lara and M. A. Labrador, IEEE Commun. Surv. Tut., 15, 1192 (2013).CrossRefGoogle Scholar
  3. 3.
    Y. Wang, L. Wang, T. Yang, X. Li, X. Zang, M. Zhu, K. Wang, D. Wu, and H. Zhu, Adv. Funct. Mater., 24, 4666 (2014).CrossRefGoogle Scholar
  4. 4.
    T. Yamada, Y. Hayamizu, Y. Yamamoto, Y. Yomogida, A. I. Najafabadi, D. N. Futaba, and K. Hata, Nat. Nanotechnol., 6, 296 (2011).CrossRefGoogle Scholar
  5. 5.
    B. J. Munro, T. E. Campbell, G. G. Wallace, and J. R. Steele, Sens. Actuator B-Chem., 131, 541 (2008).CrossRefGoogle Scholar
  6. 6.
    W. Zeng, L. Shu, Q. Li, S. Chen, F. Wang, and X. M. Tao, Adv. Mater., 26, 5310 (2014).CrossRefGoogle Scholar
  7. 7.
    K. Cherenack, C. Zysset, T. Kinkeldei, N. Münzenrieder, and G. Tröster, Adv. Mater., 22, 5178 (2010).CrossRefGoogle Scholar
  8. 8.
    V. Kaushik, J. Lee, J. Hong, S. E. Lee, S. A. Lee, J. Seo, C. Mahata, and T. Lee, Nanomaterials, 5, 1493 (2015).CrossRefGoogle Scholar
  9. 9.
    L. M. Castano and A. B. Flatau, Smart Mater. Struct., 23, 53001 (2014).CrossRefGoogle Scholar
  10. 10.
    S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers, J. Neuroeng Rehabil., 9, 1743 (2012).CrossRefGoogle Scholar
  11. 11.
    T. Cheng, Y. Zhang, W. Y. Lai, and W. Huang, Adv. Mater., 27, 3349 (2015).CrossRefGoogle Scholar
  12. 12.
    Y. Meng, Y. Zhao, C. Hu, H. Cheng, Y. Hu, Z. Zhang, G. Shi, and L. Qu, Adv. Mater., 25, 2326 (2013).CrossRefGoogle Scholar
  13. 13.
    S. Pei, J. Zhao, J. Du, W. Ren, and H. M. Cheng, Carbon, 48, 4466 (2010).CrossRefGoogle Scholar
  14. 14.
    C. Mattmann, F. Clemens, and G. Troster, Sensors, 8, 3719 (2008).CrossRefGoogle Scholar
  15. 15.
    K. Javed, C. M. A. Galib, F. Yang, C. M. Chen, and C. Wang, Synth. Met., 193, 41 (2014).CrossRefGoogle Scholar
  16. 16.
    L. Qu, M. Tian, X. Hu, Y. Wang, S. Zhu, X. Guo, G. Han, X. Zhang, K. Sun, and X. Tang, Carbon, 80, 565 (2014).CrossRefGoogle Scholar
  17. 17.
    J. Molina, J. Fernandez, J. C. Ines, A. I. del Rio, J. Bonastre, and F. Cases, Electrochim. Acta, 93, 44 (2013).CrossRefGoogle Scholar
  18. 18.
    E. Skrzetuska, M. Puchalski, and I. Krucinska, Sensors, 14, 16816 (2014).CrossRefGoogle Scholar
  19. 19.
    C. H. Lim, E. Vats, and C. S. Chan, Pattern Recognit., 48, 1773 (2015).CrossRefGoogle Scholar
  20. 20.
    V. K. Ojha, A. Abraham, and V. Snášel, Eng. Appl. Artif. Intel., 60, 97 (2017).CrossRefGoogle Scholar
  21. 21.
    G. Bosque, I. Campo, and J. Echanobe, Eng. Appl. Artif. Intel., 32, 283 (2014).CrossRefGoogle Scholar
  22. 22.
    A. Bulsari, H. Saxén, A. KrasLawski, P. Koprinkova, and M. Petrova, Eng. Appl. Artif. Intel., 5, 401 (1992).CrossRefGoogle Scholar
  23. 23.
    R. J. P. Alejo, D. S. G. González, M. C. Sifuentes, P. P. Villanueva, L. M. T. Treviño, and B. D. F. Hermosillo, Eng. Appl. Artif. Intel., 26, 1881 (2013).CrossRefGoogle Scholar
  24. 24.
    A. Balbinot and G. Favieiro, Sensors, 13, 2613 (2013).CrossRefGoogle Scholar
  25. 25.
    P. M. Granitto, P. F. Verdes, and H. A. Ceccatto, Artifi. Intell., 163, 139 (2005).CrossRefGoogle Scholar
  26. 26.
    J. Han, C. Moraga, and S. Sinne, Eng. Appl. Artif. Intel., 9, 109 (1996).CrossRefGoogle Scholar
  27. 27.
    P. Koprinkova and M. Petrova, Eng. Appl. Artif. Intel., 12, 281 (1999).CrossRefGoogle Scholar
  28. 28.
    F. M. Dias, A. Antunes, and A. M. Mota, Eng. Appl. Artif. Intel., 17, 945 (2004).CrossRefGoogle Scholar
  29. 29.
    C. L. Hui, T. W. Lau, S. F. Ng, and K. C. C. Chan, Text. Res. J., 74, 375 (2004).CrossRefGoogle Scholar
  30. 30.
    D. Teichmann, A. Kuhn, S. Leonhardt, and M. Walter, Physiol. Meas., 34, 963 (2013).CrossRefGoogle Scholar
  31. 31.
    S.-Y. Chiang, Y.-C. Kan, Y.-S. Chen, Y.-C. Tu, and H.-C. Lin, Sensors, 16, 2053 (2016).CrossRefGoogle Scholar
  32. 32.
    W. Caesarendra, T. Tjahjowidodo, Y. Nico, S. Wahyudati, and L. Nurhasanah, J. Phys.: Conf. Ser., 1007, 012005 (2018).Google Scholar
  33. 33.
    C. Vu and J. Kim, Fiber. Polym., 18, 1931 (2017).CrossRefGoogle Scholar
  34. 34.
    Z. Chen, L. Wang, and N. H. C. Yung, Pattern Recognit., 44, 2902 (2011).CrossRefGoogle Scholar
  35. 35.
    L. Wang, W. Hu, and T. Tan, Pattern Recognit., 36, 585 (2003).CrossRefGoogle Scholar
  36. 36.
    Y. Y. Jhun, S. W. Jeen, and P. C. Yen, Pattern Recognit., 29, 2213 (2008).CrossRefGoogle Scholar
  37. 37.
    S. Miyamoto, H. Ichihashi, and K. Honda, “Algorithms for Fuzzy Clustering”, pp.9–42, Springer Berlin Heidelberg, Germany, 2008.CrossRefGoogle Scholar
  38. 38.
    R.-E. Precup and H. Hellendoorn, Comput. Ind., 62, 213 (2011).CrossRefGoogle Scholar
  39. 39.
    Z. Zhang and R. Yang, Fiber. Polym., 18, 334 (2017).CrossRefGoogle Scholar
  40. 40.
    K. Liu, E. Kamalha, J. Wang, and T.-K. Agrawal, Fiber. Polym., 17, 1522 (2016).CrossRefGoogle Scholar
  41. 41.
    J. Kim and Y. Na, Fiber. Polym., 17, 963 (2016).CrossRefGoogle Scholar
  42. 42.
    F. Zhu, J. Hu, H. Zhang, J. Shi, and X. Yang, Fiber. Polym., 18, 369 (2017).CrossRefGoogle Scholar

Copyright information

© The Korean Fiber Society, The Korea Science and Technology Center 2018

Authors and Affiliations

  1. 1.Department of Organic Materials and Fiber EngineeringSoongsil UniversitySeoulKorea

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