Path Independent Gait Identification With Wi-Fi Devices

As a popular biometric for human identification,gait identification has drawn extensive attention because of its broad application prospects and economic value in the fields of security monitoring and human computer interaction.Most of the existing gait identification technologies use specialized hardware.Due to the limitations of hardware’s conditions and application environment,they can not fully meet the requirements of different application scenarios.Wi-Fi based human behavior perception has the advantages of low cost,non-invasive and ubiquitous,which can be used to make up for the shortage of specialized hardware.In this paper,gait identification using Wi-Fi Channel State Information(CSI)is studied,which mainly solves the problem of walking path restriction in the existing gait identification method,and the reliability and stability of the system are verified by a large number of experiments.In summary,the main contributions are as follows:1.Based on the channel state information of Wi-Fi,walking activity recognition is studied.In this paper,Butterworth band-pass filter is used to remove the low and high frequency noise and static component in CSI;In order to eliminate the redundancy of data and reduce the computation cost,principal component analysis is used to reduce the dimension of the denoised CSI signal;According to the different performance of power spectral density under the condition of whether there is movement or not,dynamic threshold method is adopted to detect the beginning and end of human movement;And the complete movement features are extracted from time domain and frequency domain to describe different activities;In order to achieve a stable and accurate walking activity recognition,the random forest model with two-category is used to detect the walking activity from many daily activities;The experimental results show that in several complex indoor environments,a high accuracy of walking activity detection is achieved with mean true positive rate of 96.68%,and mean false positive rate of4.61%.2.Based on the walking activity recognition,the path independent gait identification is implemented.In order to obtain path independent gait information from CSI signal,according to the measurement characteristics of CSI signal,the relationship between the body movement and the fluctuation frequency of the measured signal is established.And,Mapping it from the orthogonal measurement directions to the walking path makes that no matter what shape of the walking path it is,the gait information can be mapped to the corresponding path to obtain the path independent gait information.In order to extract sufficient and effective features to describe the human gait,the manual feature extraction and automatic feature extraction are integrated to guarantee the quantity and quality of extracted features.Then,all the features are input into the random forest model to realize a stable and reliable path independent gait identification;The experiment results in typical indoor environment demonstrate the superior performance of the system,with Top-1,Top-2,and Top-3 identification accuracies of 77.15%,87.44%,and 91.13% respectively,when the number of subjects is 50.