Code used for the experiments performed in our 2020 ICTAI submission.
Sections of this code were used in other experiments and are not used in this version. The version used for this submission should be run as-is with no code changes.
- Download our Pi 4 Activity Recognition dataset from Zenodo.
- Place the data folder in this directory.
- Run using
main.py
or the included VSCode.devcontainer
configuration.- Install dependencies if necessary via
pip install -r requirements.txt
- Install dependencies if necessary via
- Run the experiment via
main.py
The following results were achieved using the configuration parameters set for newpi
in config.json
.
Epoch 100/100
14/14 [==============================] - 1s 48ms/step - loss: 0.2284 - accuracy: 0.9266 - val_loss: 0.2829 - val_accuracy: 0.9059
[[155 0 0 0 0 0 0 0 0 0 0]
[ 0 93 34 0 0 0 0 0 0 0 0]
[ 0 80 52 1 0 0 0 1 0 0 0]
[ 0 0 0 126 0 0 0 1 15 0 0]
[ 0 0 0 0 145 0 0 0 0 0 0]
[ 0 0 0 0 0 158 0 0 0 0 0]
[ 0 0 0 0 0 0 143 0 0 0 0]
[ 0 0 0 0 0 0 0 96 0 0 0]
[ 0 0 0 0 6 0 0 0 60 0 0]
[ 0 0 0 0 0 0 0 0 0 155 0]
[ 0 0 0 0 0 0 0 0 0 0 145]]
precision recall f1-score support
nothing 1.00 1.00 1.00 155
standup 0.54 0.73 0.62 127
sitdown 0.60 0.39 0.47 134
getintobed 0.99 0.89 0.94 142
cook 0.96 1.00 0.98 145
washingdishes 1.00 1.00 1.00 158
brushteeth 1.00 1.00 1.00 143
drink 0.98 1.00 0.99 96
petcat 0.80 0.91 0.85 66
sleeping 1.00 1.00 1.00 155
walk 1.00 1.00 1.00 145
accuracy 0.91 1466
macro avg 0.90 0.90 0.90 1466
weighted avg 0.91 0.91 0.90 1466
The code in this project is licensed under MIT license. If you are using this codebase for any research or other projects, I would greatly appreciate if you could cite this repository or one of my papers.
a) "G. Forbes. CSIKit: Python CSI processing and visualisation tools for commercial off-the-shelf hardware. (2021). https://github.com/Gi-z/CSIKit."
b) "Forbes, G., Massie, S. and Craw, S., 2020, November. WiFi-based Human Activity Recognition using Raspberry Pi. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 722-730). IEEE."
@electronic{csikit:gforbes,
author = {Forbes, Glenn},
title = {CSIKit: Python CSI processing and visualisation tools for commercial off-the-shelf hardware.},
url = {https://github.com/Gi-z/CSIKit},
year = {2021}
}
@inproceedings{forbes2020wifi,
title={WiFi-based Human Activity Recognition using Raspberry Pi},
author={Forbes, Glenn and Massie, Stewart and Craw, Susan},
booktitle={2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)},
pages={722--730},
year={2020},
organization={IEEE}
}