Device-free human activity recognition using CSI
Abstract
Activity recognition is an important component of pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for device-free activity recognition. Their key limitation lies in the lack of universality. In this paper, we propose ARM, a wireless human Activity Recognition and Monitoring system. ARM investigate the correlation between CSI phase variation and human activity. We present an efficient carrier frequency offset (CFO) estimation algorithm for Wi-Fi devices and introduce Haar wavelet function to eliminate the noises. After these preprocessing, ARM uses the correlation as the profiling mechanism and recognizes a given activity by profile matching. We implemented ARM using both commercial Wi-Fi devices and USRP to evaluate it in different environments. Our result- s show that ARM achieves an average accuracy of greater than 75%.
BibTeX
@workshop{Xi-2015-122494,author = {Wei Xi and Dong Huang and Kun Zhao and Yubo Yan and Yuanhang Cai and Rong Ma and Deng Chen},
title = {Device-free human activity recognition using CSI},
booktitle = {Proceedings of 1st Workshop on Context Sensing and Activity Recognition (CSAR '15)},
year = {2015},
month = {November},
pages = {31 - 36},
}