Evaluation of machine learning methodologies on Wi-Fi activity recognition

• Main Author: Lim, Hao Zhe
• Other Authors: Li Mo
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
• Online Access: http://hdl.handle.net/10356/77186
• Institution: Nanyang Technological University

Activity recognition using Wi-Fi remains as a wide topic for many researchers due to its potential for a lower cost and wider area coverage when compared to traditional motion capturing devices and hardware. In this project, several machine learning algorithms will be applied to Wi-Fi captured packet data containing various actions performed by a user to determine which algorithms have a higher rate of classifying the right user actions. Firstly, experimental data will be captured using a router and a receiver and CSI data will be extracted through the AtherosCSI tool. Feature Selection will then be performed on the captured data to generate datasets. These generated datasets will then be put through several machine learning models to evaluate the predictive accuracies for each machine learning model. Experimental results show that using Logistic Regression and Linear Discriminant Analysis Algorithms gave the best prediction accuracy on classifying new inputs of data.