Paper Info
Title
Artificial Neural Network Based Step-Length Prediction Using Ultrasonic Sensors from Simulation to Implementation in Shoe-Type Measurement Device.
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, 50x70 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.
Year
DOI
Venue
2017
10.20965/jaciii.2017.p0321
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Keywords
Field
DocType
step-length,prediction,applied neural network,shoe-type measurement device,ultrasonic
Ultrasonic sensor,Pattern recognition,Computer science,Artificial intelligence,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
21
2
1343-0130
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Widodo, R.B.131.81
Chikamune Wada2219.55