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Contactless Continuous Activity Recognition based on Meta-Action Temporal Correlation in Mobile Environments

机译：基于Meta-Action的移动环境中的非接触式连续活动识别

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摘要

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.
机译：持续活动识别（汽车）在人类日常室内活动监测中起着重要作用，可广泛用于智能家居，人机交互和用户认证。由于隐私问题和视频信号的覆盖率有限，近年来的RF汽车引起了越来越多的关注。本文侧重于基于RF的汽车的三个关键问题：去噪，分割和识别。我们介绍了无接触和无传感器连续活动识别系统的设计和实现，即威奇克。我们的基本思想是利用连续活动中的两个相邻动作之间的时间相关性来消除连续活动分割中的累积误差。首先，使用多层优化的噪声消除方法来降低环境干扰。其次，提出了一种基于双向窗口的方法，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现，旨在评估6个连续活动序列来评估和分析影响因素。 WICHECK到两个动作的连续活动识别准确性和三种动作分别可以接近90％和75％。

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