冉亚鑫, 余江, 常俊, 李晓薇. 一种基于CSI的跌倒检测方法[J]. 云南大学学报(自然科学版), 2020, 42(2): 220-227. doi: 10.7540/j.ynu.20190415
引用本文: 冉亚鑫, 余江, 常俊, 李晓薇. 一种基于CSI的跌倒检测方法[J]. 云南大学学报(自然科学版), 2020, 42(2): 220-227. doi: 10.7540/j.ynu.20190415
RAN Ya-xin, YU Jiang, CHANG Jun, LI Xiao-wei. A method of fall detection based on CSI[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(2): 220-227. DOI: 10.7540/j.ynu.20190415
Citation: RAN Ya-xin, YU Jiang, CHANG Jun, LI Xiao-wei. A method of fall detection based on CSI[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(2): 220-227. DOI: 10.7540/j.ynu.20190415

一种基于CSI的跌倒检测方法

A method of fall detection based on CSI

  • 摘要: 针对传统人体行为识别方法系统搭建成本高、部署复杂且存在侵犯隐私等问题,提出一种使用商用Wi-Fi设备获取信道状态信息CSI进行人体行为识别与跌倒检测的方法. 通过提取信道状态信息CSI中的幅度和相位特征作为基础信号,并使用功率谱熵作为新特征建立指纹库. 采用基于人工鱼群算法AFSA修正的支持向量机SVM对动作进行分类识别,通过对SVM中的参数惩罚因子和核函数参数进行优化选择达到优化分类的效果. 根据真实环境数据验证表明,平均识别率达到94.64%.

     

    Abstract: Aiming at the problems of high cost, complex deployment and privacy violations of traditional human behavior recognition methods, a method for obtaining channel state information CSI for human behavior recognition and fall detection using commercial Wi-Fi equipment is proposed. The fingerprint database is built by extracting the amplitude and phase characteristics in the channel state information CSI as the base signal and using the power spectrum entropy as a new feature. The support vector machine (SVM) based on artificial fish swarm algorithm (AFSA) is used to classify and identify the action. The optimization of the classification is achieved by optimizing the parameter penalty factor and kernel function parameters in the SVM. According to the verification of real environmental data, the average recognition rate reached 94.64%.

     

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