A Comparison of Machine Learning Algorithms for Wi-Fi Sensing Using CSI Data

Abstract

In today’s digital era, our lives are deeply intertwined with advancements in digital electronics and Radio Frequency (RF) communications. From cell phones to laptops, and from Wireless Fidelity (Wi-Fi) to Radio Frequency IDentification (RFID) technology, we rely on a range of electronic devices for everyday tasks. As technology continues to evolve, it presents innovative ways to harness existing resources more efficiently. One remarkable example of this adaptability is the utilization of Wi-Fi networks for Wi-Fi sensing. With Wi-Fi sensing, we can repurpose existing networking devices not only for connectivity but also for essential functions like motion detection for security systems, human motion tracking, fall detection, personal identification, and gesture recognition using Machine Learning (ML) techniques. Integrating Wi-Fi signals into sensing applications expands their potential across various domains. At the Gamgee, we are actively researching the utilization of Wi-Fi signals for Wi-Fi sensing, aiming to provide our clients with more valuable services alongside connectivity and control. This paper presents an orchestration of baseline experiments, analyzing a variety of machine learning algorithms to identify the most suitable one for Wi-Fi-based motion detection. We use a publicly available Wi-Fi dataset based on Channel State Information (CSI) for benchmarking and conduct a comprehensive comparison of different machine learning techniques in the classification domain. We evaluate nine distinct ML techniques, encompassing both shallow learning (SL) and deep learning (DL) methods, to determine the most effective approach for motion detection using Wi-Fi router CSI data. Our assessment involves six performance metrics to gauge the effectiveness of each machine learning technique.