single-jc.php

JACIII Vol.21 No.2 pp. 321-329
doi: 10.20965/jaciii.2017.p0321
(2017)

Paper:

Artificial Neural Network Based Step-Length Prediction Using Ultrasonic Sensors from Simulation to Implementation in Shoe-Type Measurement Device

Romy Budhi Widodo*,** and Chikamune Wada*

*Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0196, Japan

**Informatics Engineering Study Program, Ma Chung University
Villa Puncak Tidar N-1, Malang 65151, Indonesia

Received:
September 1, 2016
Accepted:
November 15, 2016
Online released:
March 15, 2017
Published:
March 20, 2017
Keywords:
step-length, prediction, applied neural network, shoe-type measurement device, ultrasonic
Abstract

Step-length measurement as a spatial gait parameter is useful for the physician and physical therapist for determining the patient’s gait condition. We hypothesized that this could be determined using ultrasonic sensors mounted on a shoe-type measurement device. For that purpose, we have developed a shoe-type measurement device to measure gait parameters. Our system was found to effectively measure step-length and pressure distribution. However, we found that the presence of shoes leads to perishable and fragile conditions for the sensors. Therefore, we redesigned the number, angle, and range of the ultrasonic sensors mounted on the shoes in order to clarify and improve the step-length prediction. This paper discusses the improvement of a shoe-type measurement device from the implementation with real shoes and the step-length prediction using an artificial neural network (ANN). The results of the experiment show that the number, angle, and positioning of ultrasonic sensors affect their ability to capture the human step region, that is, 50×70 cm under the experimental condition of foot progression angle up to 30 degrees. The results of the predictive performance of step-length using the proposed ANN architecture demonstrate an improvement.

References
  1. [1] B. Mariani, C. Hoskovec, S. Rochat, C. Büla, J. Penders, and K. Aminian, “3D gait assessment in young and elderly subjects using foot-worn inertial sensors,” J. of Biomechanics, Vol.43, pp. 2999-3006, 2010.
  2. [2] F. Dadashi, B. Mariani, S. Rochat, C. J. Büla, B. Santos-Eggimann, and K. Aminian, “Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults,” Sensors, Vol.14, pp. 443-457, 2014.
  3. [3] C. Wada, F. Wada, K. Hachisuka, T. Ienaga, Y. Kimuro, and T. Tsuji, “Improvement study for measurement accuracy on wireless shoe-type measurement device to support walking rehabilitation,” Proc. of Int. Conf. on Complex Medical Engineering, pp. 471-474, 2012.
  4. [4] S. Ikeda and C. Wada, “Estimation of foot placement during walking by particle filter method,” The 28th Symposia of SICE Sensing Forum Measurement Division, pp. 105-108, 2011 (in Japanese).
  5. [5] R. B. Widodo and C. Wada, “Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations,” J. of Sensors, Vol.2016, Article ID 6943040, p. 24, 2016, doi:10.1155/2016/6943040.
  6. [6] R. B. Widodo and C. Wada, “Simulation of Ultrasonic Sensors in a Shoe-Type Measurement Device,” Proc. of the SICE Annual Conf., pp. 1490-1493, 2016.
  7. [7] C. Kirtley, “Clinical Gait Analysis: Theory and Practice,” Elsevier Churchill Livingstone, China, 2006.
  8. [8] D. J. Magee, “Orthopedic Physical Assessment,” 6th edition, Elsevier Saunders Canada, 2014.
  9. [9] D. Levine, J. Richards, and M. W. Whittle, “Gait Analysis,” 5th edition, Elsevier Churchill Livingstone, China, 2012.
  10. [10] B. Abernethy, V. Kippers, S. J. Hanrahan, M. G. Pandy, A. M. McManus, and L. Mackinnon, “Biophysical Foundations of Human Movement,” 3rd edition, Human Kinetics, Macmillan Education, Australia, 2013.
  11. [11] S. Walczak and N. Cerpa, “Heuristic principles for the design of artificial neural networks,” Information and Software Technology,” Vol.41, pp. 107-117, 1999.
  12. [12] T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” 2nd edition, Springer, 2008.
  13. [13] H. Yu and B. M. Wilamowski, “Levenberg-Marquardt Training,” Industrial Electronics Handbook, Vol.5 Intelligent Systems, 2nd edition, Chapter 12, pp. 12-1 to 12-15, CRC Press, 2011.
  14. [14] E. K. Antonsson and R. W. Mann, “The Frequency Content of Gait,” J. of Biomechanics, Vol.18, No.1, pp. 39-47, 1985.
  15. [15] R. Tanawongsuwan and A. Bobick, “A Study of Human Gaits Across Different Speeds,” Georgia Institute of Technology, Atlanta, 2003.
  16. [16] P. Terrier, Q. Ladetto, B. Merminod, and Y. Schutz, “High-precision Satellite Positioning System as a New Tool to Study the Biomechanics of Human Locomotion,” J. of Biomechanics, Vol.33, Issue 12, pp. 1717-1722, ISSN 0021-9290, 2000.
  17. [17] M. D. Bracker and W. J. Wooten, “Musculoskeletal Problems in Children,” Family Medicine: Principles and practice, Robert B. Taylor (editor), Vol.1, 5th edition, Springer-Verlag, New York, Inc., 1998.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jul. 25, 2017