WiFi-based human activity recognition through wall using deep learning

Wong, Yan Chiew and Ahmed Abuhoureyah, Fahd Saad and Mohd Isira, Ahmad Sadhiqin (2023) WiFi-based human activity recognition through wall using deep learning. Engineering Applications of Artificial Intelligence, 127. pp. 1-16. ISSN 0952-1976

Text 0129821122023563.PDF Restricted to Registered users only Download (13MB)

Abstract

Wireless sensing is a promising method that integrates wireless mechanisms with strong sensing capabilities. The current focus of using WiFi Channel State Information (CSI) for human activity recognition (HAR) is the line-ofsight (LoS) path, which is mainly affected by human activities and is very sensitive to environmental changes. However, the signal on non-line-of-sight (nLoS) paths, particularly those passing through walls, is unpredictable due to the weak reflected signals destroyed by the wall. This work proposes a method to achieve high-accuracy wireless sensing based on CSI behavior recognition with low-cost resources by showing through-wall and widerangle predictions using WiFi signals. The technique utilizes MIMO to exploit multipath propagation and increase the capability of signal transmission and receiving antennas. The signals captured by the multi-antenna are delivered into parallel channels with different spatial signatures. An RPi 4 B is attached to an ALFA AWUS 1900 adapter utilizing Nexmon firmware monitors and extracts CSI data with flexible C-based firmware for Broadcom/ Cypress WiFi chips. Preprocessing techniques based on CSI are applied to improve the feature extraction from the amplitude data in an indoor environment. Furthermore, a deep learning algorithm based on RNN with an LSTM algorithm is used to classify the activity instances indoors, achieving up to 97.5% accuracy in classifying seven activities. The experiment shows CSI can achieve accurate wireless sensing in nLoS scenarios with extended antennas and a deep learning approach.

Item Type:	Article
Uncontrolled Keywords:	Wireless sensing, Through wall, WiFi sensing, Human activity recognition, WiFi CSI, Deep learning, Human motion sensing, MIMO antenna system, LSTM
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	04 Jul 2024 11:16
Last Modified:	04 Jul 2024 11:16
URI:	http://eprints.utem.edu.my/id/eprint/27352
Statistic Details:	View Download Statistic

Actions (login required)

View Item