Systematic Review on Human Gait Analysis using Deep Learning Models

Abstract

Human Gait is known as a behavioral characteristic of humans, compared with the other biometrics gait is found to be a difficult process to conceal. Human gait analysis is usually done by extracting the features from the body. Analysis of gait involves evaluating the individual by means of kinematic analysis while walking along a surface. The main objective and the purpose of gait recognition is to give the best method where risks are recognized in places where there is a need for high security in any public place and to detect diseases like Parkinson’s. In order to acquire a normal person’s identification and validation performance, various Deep Learning techniques are totally studied and modeled the biometrics of gait which is based on walking data. It is reviewed that among various essential metrics that are used, deep learning convolution neural networks are typically better Machine Learning models. The main objective of the present study was to examine in detail individual gait patterns. Finally, this paper recommends deep learning methods and suggests the directions for future gait analysis and also for its applications.