PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Abstract

Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

“…Although this traditional behavior recognition method has been widely used and achieved good results, most of them require specific sensor equipment. In addition, WiFi-based behavior recognition overcomes the shortcomings of traditional methods; that is, it can automatically recognize human behavior without the user wearing a sensor or device and has been widely used in real life, including smart home, remote health care, campus security, severe illness patient care, and elderly activity detection [3].…”

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

“…The fingerprint-based localization method has been widely used in indoor localization systems because it does not need to understand the relationship between distance and signal and has a relatively high accuracy [18][19][20][21][22].…”

LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

“…In particular, the key contribution of this paper is the proposal to use features extracted from the SVD of CSI difference vectors (secant set). It is worth outlining that the use of data dimensionality reduction approaches such as Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) have been proposed in several RF sensing systems (crowd counting, activity/gesture recognition, signal fingerprinting) to reduce the instability of the collected data due to noise and hardware impairments [14]- [17]. Usually, the idea is to extract features from a transformed dataset reconstructed using the components (singular values for the SVD or principal components for the PCA) that are considered to better represent the useful information contained in the original dataset.…”