Estimation of Calcaneal Human Foot Gait Estimation from Foot Parameters Measured By a Foot Feature Measurement System Using Machine Learning Algorithms

Naidu Srinivas Kiran Babu¹ , E. Madhusudhana Reddy²

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.6 , pp.57-65, Dec-2019

Online published on Dec 31, 2019

Copyright © Naidu Srinivas Kiran Babu, E. Madhusudhana Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
An accurate and credible measurement of human gait is essential in multiple areas of medical science and rehabilitation. Yet, the methods currently available are not only arduous but also costly. Researchers who investigated the relationship between foot and gait parameters have found that the two parameters are closely interrelated and suggested that measuring foot characteristics can be an alternative to the strenuous quantification currently in use. This study aims to verify the potential of foot characteristics in predicting the actual gait temporo-spatial parameters and to develop a deep neural network (DNN) model that can estimate and quantify the gait temporo-spatial parameters from foot characteristics. This research studies the relationship between footprint depths and load in the calcaneal area when human standing in an upright posture. Footprint depths are deformation in the calcaneal area obtained from the z-value extraction of the Boolean operation acquired from unloaded foot scanning using 3D scanner and loaded foot using foot plantar scanner. To compare peak loads estimated from footprint depth maximum, force sensing resistor (FSR) sensor is attached over the shoe insole with zero heel height in the calcaneal area.It is crucial to find methods that analyze large amount of data captured by cameras and/or various sensors installed all around us. Machine learning becomes a prevailing tool in analyzing such data that signifies behavioral characteristics of human beings. Gait as an identifier for use in individual recognition systems has respective and almost certainly unique key features for each person including centroid, cycle length and step size. Gait is sometimes preeminent suited to recognition or surveillance scenarios. It might be used in the identification of females who are wearing veils in some countries without critical social issues. The objective of this project is to predict accurately one-dimensional coordinates of normalized n-component vectors representing two dimensional silhouettes in order to identify individuals at a distance without any interaction and obtrusion. Varied algorithms are further incorporated into walk pattern analysis to adoptively improve gait recognitions and classification. The results are reported reasonable identification performance as compared to several machine learning methods.

Key-Words / Index Term :
Gait Recognition, Feature Vectors, Machine Learning and Classification Methods, calcaneal shift, 3d scanner foot print, SVM, neural network

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