Human Gait Recognition: (Analysis and Classification)

Abstract

Gait State Analysis refers to the way in which an individual walks. With every individual having a unique walking pattern, it can be used as a biometric in many domains like sports, medicine, and many more. The gait state analysis can be used as a biometric without any contact with the individual, making it a very versatile methods for many purposes. Gait recognition has two major steps, first the data is to be collected and stored, then from the parameters calculated using that data, mechanisms to identify an individual are applied. The first step can be done by using two methods: Wearable Sensors and Non-Wearable Sensors. Types of Wearable sensors explained are: Floor Sensors and Image Processing Sensors; Types of Non-Wearable sensors explained are: Force and pressure sensors, Sensing Fabric and Electromagnetic Tracking System. Once the data is collected and stored, it can be used for the recognition of an individual by means of ANN (Artificial Neural Network Deep Learning), Inductive Machine Learning, and Fuzzy Logic mechanisms.