Artificial Neural Network Optimization with Levenberg–Maruardt Algorithm for Dynamic Gesture Recognition

  • Authors

    • Stephen John Dy
    • Matthew Adrianne Gonzales
    • Lenard Lozano
    • Miguel Angelo Suniga
    • Alexander Abad
    2018-07-27
    https://doi.org/10.14419/ijet.v7i3.13.16312
  • Gesture Recognition, Industrial Robots, Neural Networks, Optimization, Robotics

  • Movement has long been a mode of expression and communication. A challenge arises when we try to bestow the ability to learn and recognize movements to machines, specifically computers, but with the development of sensor technology and the growing interest in machine learning algorithms, there is an opportunity to explore and formulate new approaches. The study focuses on the use of the Levenberg Marquardt Algorithm as an optimization algorithm for a multilayer Artificial Neural Network in constructing a predictive model for dynamic gestures. Extraction of the data set was made integral to the research. The study concludes that the network architecture is adequate for gesture recognition, with an average recognition rate of 83%, but a larger data set may show to improve this value.

     

     

     

  • References

    1. [1] G. R. S. Murthy and R. S. Jadon, "A Review of Vision Based Hand Gestures Recognition," International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 405-410, 2009.

      [2] N. A. P. Garg and S. Sofat, "Vision Based Hand Gesture Recognition," World Academy of Science, Engineering and Technology, vol. 49, pp. 972-977.

      [3] B. M. Lee-Cosio, C. Delgado-Mata and J. Ibanez, "ANN for Gesture Recognition using Accelerometer Data," The 2012 Iberoamerican Conference on Electronics Engineering and Computer Science, no. 3, pp. 109-120, 2012.

      [4] S. Naik, A. HR, A. KN and B. SP, "A Study on Automotive Human Vehicle Interaction Using Gesture Recognition Technology," International Journal of Multidisciplinary in Cryptology and Information Security, vol. 1, no. 2, pp. 6-12, 2012.

      [5] N. Ibraheem, M. Hasan, R. Khan and P. Mishra, "Comparative Study of Skin Color based Segmentation Techniques," 2012.

      [6] X. Li, "Gesture Recognition Based on Fuzzy C-Means Clustering Algorithm," 2003.

      [7] S. Mitra and T. Acharya, "Gesture Recognition: A Survey," EEE Transactions on systems, Man and Cybernetics, Part C: Applications and reviews, vol. 37, no. 3, pp. 311-324, 2007.

      [8] L. Villanueva and R. C. Gustilo, "Artificial neural network based antenna sensitivity assignments for chaotic internet service provider network architecture," International Journal of Engineering & Technology, vol. 7, no. 2.3, pp. 14-17, 2018.

      [9] C. B. Bangal, "Automatic Generation Control of Interconnected Power Systems Using Artificial Neural NetworkTechniques," 2009.

      [10] Y. Huang, "Advances in Artificial Neural Networks – Methodological," Algorithms, vol. 2, pp. 973-1007, 2009.

      [11] H. P. Gavin, "The Levenberg-Marquardt method for nonlinear least squares curve-fitting problem," 22 March 2017. [Online]. Available: http://people.duke.edu/~hpgavin/ce281/lm.pdf. [Accessed April 2018].

      [12] R. Wang, "Levenberg-Marquardt algorithm," 12 February 2015. [Online]. Available: http://fourier.eng.hmc.edu/e176/lectures/NM/node37.html. [Accessed 28 April 2018].

      [13] S. K. Yewale and P. K. Bharne, "Hand gesture recognition using different algorithms based on artificial neural network," in 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Udaipur, India, 2011.

      [14] H. Yu and B. M. Wilamowski, "Levenberg–Marquardt Training," in Industrial Electronics Handbook, vol. 5 – Intelligent Systems, 2nd Edition, chapter 12, CRC Press, 2010, pp. 12-1 to 12-15.

  • Downloads

  • How to Cite

    John Dy, S., Adrianne Gonzales, M., Lozano, L., Angelo Suniga, M., & Abad, A. (2018). Artificial Neural Network Optimization with Levenberg–Maruardt Algorithm for Dynamic Gesture Recognition. International Journal of Engineering & Technology, 7(3.13), 1-4. https://doi.org/10.14419/ijet.v7i3.13.16312