【摘要】 目前,基于Wi-Fi的无线人员感知技术被广泛应用于防入侵安全监测、人类健康护理、步态识别等领域,对此提出了一种基于无设备的非合作式人体行为识别方法,利用Wi-Fi信号的信道状态信息CSI来识别5个动态活动:行走、坐-站、深蹲、跳跃和跌倒。该方法利用SIMO系统采集CSI数据,在对CSI幅度和相位分别进行预处理之后,实施3个步骤来降低计算开销机制:子载波融合、基于移动方差阈值的不良数据链路剔除以及基于小波变换的动态时间窗口的数据分割。在经过前期的各项预处理后提取动作特征,从时域扩展到频率域,通过分析多普勒功率谱的特性来提高CSI信号的利用率。实验结果表明,总体识别率随着使用特征维度的增加而上升;组合分类器加权投票方法经过两轮投票优化,把对5个动作的总体识别率提高到90.3%,且相较于RSSI,CSI在人体行为识别领域的优势更加明显。更多还原

【Abstract】 Currently,Wi-Fi-based wireless personnel perception technology is widely used in anti-intrusion security monitoring,human health care,gait recognition and other fields,regarding this,this paper proposed a non-cooperative human behavior recognition method.The channel state information(CSI) of Wi-Fi signals can be used to recognize five dynamic activities:walking,sitting-standing up,squatting,jumping and falling.The method uses a SIMO system to collect CSI data,and after performing pre-processing on the CSI amplitude and phase respectively,implements a three-step computational cost reduction mechanism:subcarrier fusion,rejection of bad data link based on mobile variance threshold,and data segmentation of dynamic time window based on wavelet transform.Then activity features are extracted and extended from the time domain to the frequency domain.By analyzing the characteristics of the Doppler power spectrum,the utilization of the CSI signal is improved.Experiment results show that the overall recognition rate increases with the use of feature dimensions.Optimized by two rounds of voting,the combined classifier weighted voting method is increasing the overall recognition rate of five dynamic activities to 90.3%.And compared to RSSI,the advantages of CSI in the field of human behavior recognition are more prominent.更多还原