Neural Network-Based Multiplicatively Gait Feature Eradication and Detection

Abstract

This research proposes an innovative method based on machine learnings for extracting and identifying gait features from multiple sources. The method aims to enhance the accuracy of gait identification by minimizing interferences caused by complex backgrounds and shelters, thereby capturing more precise information that reflects the walking characteristics of moving individuals. The technical approach involves the acquisition of gait data using a video recorder and a pyroelectric IR sensor. The image source information obtained from the video recorder is utilized to extract skeleton feature variables and Radon difference peak characteristic variables. In addition, the pyroelectric IR source information is transformed from a voltage signal to frequency domain characteristic variables. These variables are then merged after undergoing dimension reduction and signal processing. Finally, a backpropagation neural network is employed as the classifier to perform classified identification based on the merged characteristics, and the identification accuracy is evaluated. The primary application of this method is in the field of identification.