PAWS: Passive Human Activity Recognition Based on WiFi Ambient Signals
PAWS: Passive Human Activity Recognition Based on WiFi Ambient Signals
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PAWS：基于 WiFi 环境信号的被动人体活动识别
DOI：
10.1109/jiot.2015.2511805
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发表时间：
2016-10-01
影响因子：
10.6
通讯作者：
Li, Jie
中科院分区：
文献类型：
--
作者：
Gu, Yu;Ren, Fuji;Li, Jie
室内人体活动识别是近几十年来的研究热点。然而，以往的解决方案要么依赖于特殊的硬件，要么需要主体的合作。因此，可扩展性问题仍然是一个巨大的挑战。为此，我们提出了一个在线活动识别系统，它探讨了WiFi环境信号的接收信号强度指示器（RSSI）指纹的不同活动。它可以集成到任何现有的WLAN网络中，而无需额外的硬件支持。此外，它不需要主体在识别过程中合作。更具体地说，我们首先进行实证研究，以深入了解WiFi特性，例如，活动对WiFi RSSI的影响。然后，我们提出了一个在线活动识别架构，是灵活的，可以适应不同的设置/条件/场景。最后，一个原型系统的建立和评估，通过广泛的现实世界的实验。为了更好地对具有相似特征的活动进行分类，基于分类树设计了一种新的融合算法。实验结果表明，融合算法优于其他三个众所周知的分类器[即，NaiveBayes，Bagging和k-nearest neighbor（k-NN）]的准确性和复杂性。重要的景点和实践经验，已获得指导系统的实施和概述未来的研究方向。
Indoor human activity recognition remains a hot topic and receives tremendous research efforts during the last few decades. However, previous solutions either rely on special hardware, or demand the cooperation of subjects. Therefore, the scalability issue remains a great challenge. To this end, we present an online activity recognition system, which explores WiFi ambient signals for received signal strength indicator (RSSI) fingerprint of different activities. It can be integrated into any existing WLAN networks without additional hardware support. Also, it does not need the subjects to be cooperative during the recognition process. More specifically, we first conduct an empirical study to gain in-depth understanding of WiFi characteristics, e.g., the impact of activities on the WiFi RSSI. Then, we present an online activity recognition architecture that is flexible and can adapt to different settings/conditions/scenarios. Lastly, a prototype system is built and evaluated via extensive real-world experiments. A novel fusion algorithm is specifically designed based on the classification tree to better classify activities with similar signatures. Experimental results show that the fusion algorithm outperforms three other well-known classifiers [i.e., NaiveBayes, Bagging, and k-nearest neighbor (k-NN)] in terms of accuracy and complexity. Important sights and hands-on experiences have been obtained to guide the system implementation and outline future research directions.