FreeSense:Indoor Human Identification with WiFi Signals

TL;DR

FreeSense leverages WiFi signals and advanced signal processing to identify individuals indoors non-intrusively, achieving high accuracy in domestic environments without compromising privacy or coverage.

Contribution

This paper introduces a novel WiFi-based human identification method using CSI analysis, combining PCA, DWT, and DTW techniques, with experimental validation in a smart home setting.

Findings

Identification accuracy ranges from 88.9% to 94.5%.

Effective with a small candidate set of 2 to 6 users.

Non-intrusive and preserves user privacy.

Abstract

Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.