الفهرس 
Human Activity Recognition based on Wi-Fi CSIOnly 14 pages are availabe for public view
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Abstract
In recent years, channel state information (CSI) in Wi-Fi 802.11n has been increasingly used to collect data pertaining to human activity. Such raw data are then used to enhance human activity recognition. Activities such as lying down, falling, walking, running, sitting down, and standing up can now be detected with the use of information collected through CSI. Human activity recognition has a multitude of applications, such as home monitoring of patients. Four deep learning models are presented in this thesis, namely: a convolution neural network (CNN) with a Gated Recurrent Unit (GRU); a CNN-GRU with attention; a CNN-GRU-CNN, and a CNN with Long Short-Term Memory (LSTM) and a second CNN. Those models were trained to perform Human Activity Recognition (HAR) using CSI amplitude data collected by a CSI tool. Experiments conducted to test the efficacy of these models showed superior results compared with other recent approaches. This enhanced performance of our proposed models may be attributable the ability of these models to make full use of available data and to extract all data features including high dimensionality and time sequence. The highest average recognition accuracy reached by the proposed models was achieved by the CNN-GRU, and the CNN-GRU with attention models, standing at 99.31% and 99.16% respectively.