Continuous activity recognition (CAR) plays an important role in human daily indoor activity monitoring and can be widely used in smart home, human-computer interaction and user authentication. Due to the privacy issue and limited coverage of video signals, RF-based CAR has attracted more and more attention in recent years. This paper focuses on three key problems in RF-based CAR: denoising, segmentation and recognition. We present the design and implementation of a contactless and sensorless continuous activity recognition system, namely WiCheck. Our basic idea is to utilize the temporal correlation between two adjacent actions in continuous activity to eliminate the cumulative error in continuous activity segmentation. Firstly, the multi-layer optimized noise elimination method is used to decrease the environment interference. Secondly, a method based on dual-swing window is proposed to reduce the cumulative error of continuous activity segmentation. Finally, WiCheck is implemented in different indoor environments, and 6 continuous activity sequences are designed to evaluate and analyze the influencing factors. The continuous activity recognition accuracy of WiCheck to two actions and three actions can approach 90% and 75%, respectively.

连续活动识别（CAR）在人类日常室内活动监控中起着重要作用，可广泛用于智能家居，人机交互和用户身份验证。由于隐私问题和视频信号的覆盖范围有限，基于RF的CAR近年来引起了越来越多的关注。本文关注基于RF的CAR的三个关键问题：去噪，分割和识别。我们介绍了非接触和无传感器连续活动识别系统，即WiCheck的设计和实现。我们的基本思想是利用连续活动中两个相邻动作之间的时间相关性来消除连续活动分段中的累积误差。首先，采用多层优化的噪声消除方法来减少环境干扰。其次，提出了一种基于双摆动窗口的方法，以减少连续活动分割的累积误差。最后，WiCheck在不同的室内环境中实施，并设计了6个连续的活动序列来评估和分析影响因素。WiCheck对两个动作和三个动作的连续活动识别准确性分别可以达到90％和75％。