Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks

Abstract

Object detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction prediction system based on a Single-Input-Single-Output (SISO) communication channel model and Shallow Neural Network (SNN). The motion direction prediction accuracy of SNN is highlighted against the other types of Machine Learning (ML) models. The comparative analysis of models in this study shows that unique human movement patterns, superimposed on received pilot radio signal, can be classified precisely by SNN, with an accuracy of approximately 89.13% compared to the other ML based models. The results of this study would guide scholars, active in developing human motion recognition systems, intrusion detection systems, or Well-being and healthcare systems, and in processes that innovate and improve processing techniques for monitoring and control.

Keywords

• Artificial intelligence
• artificial neural networks
• classification algorithms
• data analysis
• feature extraction
• feedforward neural networks