Human Movement Recognition using Deep Learning on Visualized CSI Wi-Fi data

Wireless signal transmission is an intricate process, significantly influenced by the environment within which it operates. Notably, the mobility of various elements within this environment, such as the parts of a human body, distinctly modifies the manner in which these signals are reflected. These alterations subsequently cause changes in Channel State Information (CSI) data captured by Wi-Fi routers. Intriguingly, specific human behaviors can be detected through a meticulous examination of the data streams from CSI. These behaviors, representing diverse activities, can be identified by processing the data streams and juxtaposing them against predefined models. The recognition of these activities hinges on discerning patterns within the CSI data, reflecting the relationship between human movement and the variation in channel state information. A variety of techniques have been developed to explore and understand these patterns, with machine learning emerging as the most popular and effective tool. Machine learning techniques are harnessed to develop sophisticated models capable of correlating variations in channel state information with specific human movements. These correlations enable the prediction and identification of human activities based on changes in CSI data. This research focuses on further exploring this intriguing intersection of human activity, wireless signal processing, and machine learning. It aims to provide a deeper understanding of these correlations and develop more effective models for human activity recognition. More specifically, with this work we attempt to to explore new way of using the CSI data in Deep Learning tasks. That is by using the visualized amplitude of signals and correlate them to certain activities

Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig