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• W2959586908 abstract "Existing indoor semantic recognition schemes are capable of discovering patterns through smartphone sensing; however, there is a lack of a device-free indoor semantic recognition system. In this paper, we propose WiFiMap+, which is a first-ever automatical inference system using WiFi signals to recognize highlevel indoor semantics from human activities and environments, where the high-level indoor semantics consist of indoor facilities and environments. To characterize the static indoor environments and dynamical human activities separately with channel state information (CSI), we propose a novel two-stream architecture to generate the spatial streams and the movement streams independently. Compared to the recent research on activity recognition, this two-stream architecture can make the content area of CSI samples extend from human activities to indoor environments. For obtaining accurate indoor environment detection, we propose a CSI-environment model with a spatial stream generation algorithm, which can reduce the effect of human activities on environment detection. For stable activity recognition, we also propose an environment-based testing sample representation method, which can utilize the environment knowledge to overcome the diversity of CSI caused by the environment changes. Finally, we implement WiFiMap+ using commercial WiFi devices and evaluate its performance for seven common semantic detection cases in sixroom scenarios. The experimental results show that our proposed WiFiMap+ is robust to the multi-room scenario and can achieve the average accuracy of 92.8% and the lowest accuracy of about 82%." @default.
• W2959586908 created "2019-07-23" @default.
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• W2959586908 date "2019-08-01" @default.
• W2959586908 modified "2023-09-24" @default.
• W2959586908 title "WiFiMap+: High-Level Indoor Semantic Inference With WiFi Human Activity and Environment" @default.
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• W2959586908 doi "https://doi.org/10.1109/tvt.2019.2926844" @default.
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