- 學位論文
基於 WiFi 通道狀態資訊的跌倒偵測系統研究及改進
A Study on a Device-Free Fall Detection System Based on WiFi Channel State Information and its Improvement
摘要
跌倒運動檢測是近年來學術和應用領域最引人注目的人體運動檢測，因為跌倒是現代社會老年人致命健康威脅的主要原因，甚至可能導致死亡。由於跌倒會產生比一般日常運動更高的速度，我們使用速度資訊來檢測跌倒運動。速度資訊非常適用於在有偌大速度差異運動中的跌倒檢測，例如走路和跌倒。然而，我們很難只用速度來區分有速度差別不顯著的運動，例如跌倒和奔跑。因此，我們設計了一種新穎的跌倒檢測系統，該系統利用基於通道狀態信息 (CSI) 功率響應的自相關函數(autocorrelation function, ACF)的速度資訊以及機器學習模型的集成方法實現。首先，我們使用 PicoScenes 平台從常見的 WiFi 信號中收集 PHY 層細粒度的CSI。在接收到CSI信號後，我們設計了一種新的第一局部谷值演算法來尋找CSI功率響應的自相關函數（ACF）的第一局部谷值的位置。然後，藉由該位置計算出速度資訊。結果表明，我們通過演算法估計的速度具有代表性，因為估計的速度幾乎與實際速度成正比。因此，我們利用此速度資訊進行後續跌倒偵測，並將其視為二元分類任務，因為只有兩類數據：有跌倒和無跌倒。為了完成二元分類，我們在速度資訊上使用機器學習模型的檢查點集成方法的擴展，該模型可以區分有細微速度差別的運動，特別是對於卷積神經網絡（CNN）。使用此系統性能可以提高 29% 且誤判率可以降低 30.5%。最終，我們將此系統應用於多人環境，準確率為 90% 且精確度可以到達 94%。
並列摘要
Fall motion detection is the most high-profile human motion detection in academic and industrial regions in recent years since fall is the leading cause of fatal health threats for elders in modern society that can even give rise to death. Since fall leads to a higher speed than general daily motions, we employ speed information to detect fall motion. The speed works well on fall detection in the motions with a huge difference in speed, such as walking and falling. Nevertheless, it is impossible to use only speed to distinguish the motions with a slight difference in speed, such as falling and running. Therefore, we design a novel fall detection system leveraging a machine learning model with the ensemble method and speed information based on the autocorrelation function (ACF) of Channel State Information (CSI) power response. First, we collect PHY layer fine-grained CSI from ubiquitous WiFi signals with the PicoScenes platform. After receiving the CSI signal, we design a new first local valley algorithm to find the location of the first local valley of the autocorrelation function (ACF) of CSI power response. Then, the speed information is calculated from this location. The result shows that our estimated speed by the algorithm is representative since the estimated speed is almost proportional to the real speed. Thus, we leverage this speed information for the following fall detection and regard it as a binary classification task since there are only two classes of data: fall class and no fall class. To accomplish binary classification, we perform the machine learning model with the extension of the checkpoint ensemble techniques on the speed information, which can distinguish the motions with a slight difference in speed, especially for the Convolutional Neural Network (CNN). The performance is improved by 29% and the false alarm rate is decreased by 30.5%. Eventually, we apply it to the multi-person environment. The accuracy is 90% and the precision reaches 94%.