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

Paper:

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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
Creative Commons License
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.
Cite this article as:
R. Widodo and C. Wada, “Artificial Neural Network Based Step-Length Prediction Using Ultrasonic Sensors from Simulation to Implementation in Shoe-Type Measurement Device,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.2, pp. 321-329, 2017.
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