WiFi和安全性既是一个问题，也是日常生活中越来越多的存在。今天的运动检测实现在保密性、范围和成本方面严重不足。为了解决这个问题，我们的目标是开发一种运动检测系统，利用WiFi信道状态信息（CSI），它描述了无线信号如何从发射机传播到接收机。本研究的目标是开发一个实时的运动检测和分类系统，是谨慎的，具有成本效益，易于实现。该系统只需要一台配备Intel Ultimate N WiFi Link 5300和标准路由器的Ubuntu笔记本电脑。该系统将分为两个部分：（1）一个强大的系统，以跟踪CSI的变化，在实时，和（2）算法，以分类的运动。用于实时跟踪CSI差异的系统已于2018年8月完成。初步结果表明，引入运动到一个先前静止的区域检测具有高的置信度。我们提出了（1）异常检测的发展，利用在初始程序和/或无监督机器学习中实现的移动平均滤波器，以及（2）监督机器学习算法，使用提出的特征提取方法对一组简单运动进行分类。最后，分类方法，如决策树，朴素贝叶斯和长短期记忆，可用于分类基本动作，无论速度，位置或方向。
英文摘要：
WiFi and security pose both an issue and act as a growing presence in everyday life. Today's motions detection implementations are severely lacking in the areas of secrecy, scope, and cost. To combat this problem, we aim to develop a motion detection system that utilizes WiFi Channel State Information (CSI), which describes how a wireless signal propagates from the transmitter to the receiver. The goal of this study is to develop a real-time motion detection and classification system that is discreet, cost-effective, and easily implementable. The system would only require an Ubuntu laptop with an Intel Ultimate N WiFi Link 5300 and a standard router. The system will be developed in two parts: (1) a robust system to track CSI variations in real-time, and (2) an algorithm to classify the motion. The system used to track CSI variance in real-time was completed in August 2018. Initial results show that introduction of motion to a previously motionless area is detected with high confidence. We present the development of (1) anomaly detection, utilizing the moving average filter implemented in the initial program and/or unsupervised machine learning, and (2) supervised machine learning algorithms to classify a set of simple motions using a proposed feature extraction methods. Lastly, classification methods such as Decision Tree, Naive Bayes, and Long Short-Term Memory can be used to classify basic actions regardless of speed, location, or orientation.
Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究实验室）；Department of Computer Science, Brunel University London（布鲁内尔大学伦敦计算机科学系）；Pervasive Communication Research Center, Purple Mountain Laboratories（紫金山实验室 pervasive 通信研究中心）
