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http://dx.doi.org/10.3837/tiis.2020.06.004

A Robust and Device-Free Daily Activities Recognition System using Wi-Fi Signals  

Ding, Enjie (IoT Perception Mine Research Center, China University of Mining and Technology)
Zhang, Yue (IoT Perception Mine Research Center, China University of Mining and Technology)
Xin, Yun (IoT Perception Mine Research Center, China University of Mining and Technology)
Zhang, Lei (School of Information and Electrical Engineering, Xuzhou University of Technology)
Huo, Yu (IoT Perception Mine Research Center, China University of Mining and Technology)
Liu, Yafeng (IoT Perception Mine Research Center, China University of Mining and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.6, 2020 , pp. 2377-2397 More about this Journal
Abstract
Human activity recognition is widely used in smart homes, health care and indoor monitor. Traditional approaches all need hardware installation or wearable sensors, which incurs additional costs and imposes many restrictions on usage. Therefore, this paper presents a novel device-free activities recognition system based on the advanced wireless technologies. The fine-grained information channel state information (CSI) in the wireless channel is employed as the indicator of human activities. To improve accuracy, both amplitude and phase information of CSI are extracted and shaped into feature vectors for activities recognition. In addition, we discuss the classification accuracy of different features and select the most stable features for feature matrix. Our experimental evaluation in two laboratories of different size demonstrates that the proposed scheme can achieve an average accuracy over 95% and 90% in different scenarios.
Keywords
CSI; human activities recognition; phase transformation; device-free system; classification algorithms;
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