基于无线信道状态信息的跌倒无源监测方法

Passive falling detection method based on wireless channel state information

针对传统基于视频或传感器的跌倒检测方法中环境依赖、空间受限等问题,提出了一种基于无线信道状态信息的跌倒无源检测方法Fallsense。该方法利用普适、低成本的商用WiFi设备,首先采集无线信道状态数据并对数据进行预处理,然后设计动作—信号分析模型,建立轻量级动态模板匹配算法以从时序信道数据中实时检测出承载真实跌倒事件的相关片段。大量实际环境下的实验表明, Fallsense可以实现较高的准确率以及较低的误报率,准确率达到95%,误报率为2.44%。与经典WiFall系统相比,Fallsense将时间复杂度从WiFall的O(mN log N)降低到O(N)(N是样本数,m是特征数),且准确率提高了2.69%,误报率下降了4.66%。实验结果表明,所提方法是一种快速高效的无源跌倒检测方法。

Traditional vision-based or sensor-based falling detection systems possess certain inherent shortcomings such as hardware dependence and coverage limitation, hence Fallsense, a passive falling detection method based on wireless Channel State Information(CSI) was proposed. The method was based on low-cost, pervasive and commercial WiFi devices. Firstly, the wireless CSI data was collected and preprocessed. Then a model of motion-signal analysis was built, where a lightweight dynamic template matching algorithm was designed to detect relevant fragments of real falling events from the time-series channel data in real time. Experiments in a large number of actual environments show that Fallsense can achieve high accuracy and low false positive rate, with an accuracy of 95% and a false positive rate of 2.44%. Compared with the classic WiFall system, Fallsense reduces the time complexity from O(mN log N) to O(N)(N is the sample number, m is the feature number), and increases the accuracy by 2.69%, decreases the false positive rate by 4.66%. The experimental results confirm that this passive falling detection method is fast and efficient.