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README.md

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 # CSI-Activity-Recognition
-Human Activity Recognition using Channel State Information
+
+Human Activity Recognition using Channel State Information for Wifi Applications
+
+A simple Tensorflow 2.0+ model using Bidirectional LSTM stacked with one Attention Layer.
+
+This code extends the previsous work of paper [A Survey on Behaviour Recognition Using WiFi Channel State Information](https://ieeexplore.ieee.org/document/8067693/) ([corresponding code](https://github.com/ermongroup/Wifi_Activity_Recognition)).
+
+## Dataset Preparation
+
+Download the public dataset from [here](https://drive.google.com/file/d/19uH0_z1MBLtmMLh8L4BlNA0w-XAFKipM/view?usp=sharing).
+
+unzip the Dataset.tar.gz by the following command:
+
+```bash
+tar -xzvf Dataset.tar.gz
+```
+
+Inside the dataset, there are 7 different human activities: `bed`, `fall`, `pickup`, `run`, `sitdown`, `standup` and `walk`.
+
+## Requirements
+
+Numpy
+
+Tensorflow 2.0+
+
+sklearn
+
+## Performance of the Model with Default Parameters
+
+## Default Parameters
+
+| Parameters for Batching Sequence  |  Value  |  
+|-------------------|:-------------:|
+| window length     |  1000 |
+| Sliding Steps     |  200   |
+| Downsample Factor |  2 |
+| Activity Present Threshold | 0.6 (60%)|
+
+| Parameters for Deep Learning Model  |  Value  |  
+|-------------------|:-------------:|
+| # of units in Bidirectional LSTM     |  200 |
+| # of units in Attention Hidden State     |  400 |
+| Batch Size |  128 |
+| Learning Rate | 1e-4|
+| Optimizer | Adam |
+| # of Epochs | 60 |
+
+## Model Architecture
+
+![Architecture](https://github.com/ludlows/CSI-Activity-Recognition/raw/master/img/model.png)
+
+## Confusion Matrix
+
+![Confusion Matrix](https://github.com/ludlows/CSI-Activity-Recognition/raw/master/img/confusion_matrix.png)
+
+| Label  |  Accuracy  |  
+|-------------------|:-------------:|
+| bed     |  100% |
+| fall    |  97.18%   |
+| pickup  |  98.68% |
+| run     | 100% |
+| sitdown |  95%  |
+| standup |   95.56% |
+| walk    | 99.51% |
+
+## Usage
+
+Download the code from github.
+
+```bash
+git clone https://github.com/ludlows/CSI-Activity-Recognition.git 
+```
+
+Enter the code folder.
+
+```bash
+cd CSI-Activity-Recognition
+```
+
+## Run The Model with Default Parameters
+
+```bash
+python csimodel.py your_raw_Dataset_folder
+```
+
+Meanwhile, you could also modify the parameters in the `csimodel.py` or change the architectures of neural networks.
+
+This code could be a starting point for your deep learning project using Channel State Information.