Device-Free Activity Recognition Based on Coherence Histogram

Abstract

Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.