  WiFi and security pose both an issue and act as a growing presence in
everyday life. Today's motions detection implementations are severely lacking
in the areas of secrecy, scope, and cost. To combat this problem, we aim to
develop a motion detection system that utilizes WiFi Channel State Information
(CSI), which describes how a wireless signal propagates from the transmitter to
the receiver. The goal of this study is to develop a real-time motion detection
and classification system that is discreet, cost-effective, and easily
implementable. The system would only require an Ubuntu laptop with an Intel
Ultimate N WiFi Link 5300 and a standard router. The system will be developed
in two parts: (1) a robust system to track CSI variations in real-time, and (2)
an algorithm to classify the motion. The system used to track CSI variance in
real-time was completed in August 2018. Initial results show that introduction
of motion to a previously motionless area is detected with high confidence. We
present the development of (1) anomaly detection, utilizing the moving average
filter implemented in the initial program and/or unsupervised machine learning,
and (2) supervised machine learning algorithms to classify a set of simple
motions using a proposed feature extraction methods. Lastly, classification
methods such as Decision Tree, Naive Bayes, and Long Short-Term Memory can be
used to classify basic actions regardless of speed, location, or orientation.
