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, 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.