Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

TL;DR

Wi-Motion is a WiFi-based human activity recognition system that leverages amplitude and phase information from CSI to accurately classify six activities with high precision.

Contribution

Wi-Motion introduces a novel approach combining amplitude and phase CSI data with a posterior probability strategy for improved activity recognition.

Findings

Achieves 98.4% accuracy in recognizing six human activities

Utilizes amplitude and phase information separately for classification

Demonstrates robustness over existing WiFi-based recognition methods

Abstract

Recent research has shown that human motions and positions can be recognized through WiFi signals. The key intuition is that different motions and positions introduce different multi-path distortions in WiFi signals and generate different patterns in the time-series of channel state information (CSI). In this paper, we propose Wi-Motion, a WiFi-based human activities recognition system. Unlike existing systems, Wi-Motion adopts the amplitude and phase information extracted from the CSI sequence to construct the classifiers respectively, and combines the results using a combination strategy based on posterior probability. As the simulation results shows, Wi-Motion can recognize six human activities with the mean accuracy of 98:4%.