Prediction of Sagittal Curve Profile of Rollover Footwear Based on Ankle Kinematics while Walking by Applying Neural Network Techniques

Abstract

Introduction: Sagittal rocker profiles are one of the most commonly prescribed therapeutic footwear interventions to alter or adapt lower limb joints’ kinematics and kinetics. However, the prescription criteria for rocker profiles are commonly based on theoretical considerations. Thus, conducting experimental studies and experiment and error may result in their better prescription and use. A complementary approach is to use intelligent technology to predict curve profile to suit a specific joint position. The aim of this study was to predict sagittal curve profile of the rollover footwear from ankle kinematics while walking by applying an artificial neural network (ANN). Materials and Methods: In the present study, 20 healthy participants (with mean age of 33.1 years) walked on a straight path for 10 meters wearing two different shoes with two different sole curved profiles and ankle kinematic data were collected using reflective markers. The ANN was trained to associate set of ankle sagittal plane motions during stance phase with outsole curve profiles, and then, predict the latter based on the former. The ANN was trained using the data from 13 participants (control group) to obtain the model and the data from the remaining participants (intervention group) was used for the validation of the study purposes. Results: The achieved accuracy was very satisfactory, since the correlation coefficients between the predicted output and the actual curve profile in the validation data were higher than 0.95 for both types of rollover footwear. Conclusion: In this study, a novel algorithm was proposed for sole curve profile characterization of rollover footwear using an ANN model. The results of this study may be useful to designers of footwear, lower limb prostheses, orthoses, and walking casts/boots.