WiFi Motion Detection: A Study into Efficacy and Classification

TL;DR

This paper explores WiFi Channel State Information (CSI) for real-time, discreet motion detection and classification, aiming to improve scope, cost, and privacy over existing methods.

Contribution

It develops a WiFi-based system utilizing CSI variations for real-time motion detection and classification with machine learning algorithms, demonstrating high-confidence detection of motion.

Findings

High-confidence detection of motion introduction in static areas

Effective classification of simple motions using machine learning

System is low-cost and easily implementable with standard hardware

Abstract

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.