This repo contains the code for the experiments in the papers:
- Yu Hu, Yongkang Wong, Wentao Wei, Yu Du, Mohan Kankanhalli, Weidong Geng. " A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition"
- Yu Hu, Yongkang Wong, Qingfeng Dai, Mohan Kankanhalli, Weidong Geng. " sEMG-based gesture recognition with embedded virtual hand poses and adversarial learning"
- A CUDA compatible GPU
- Ubuntu >= 14.04 or any other Linux/Unix that can run Docker
- Docker
- Nvidia Docker
-
Pull docker image for the first paper
docker pull zjucapg/semg:latest
-
Pull docker image for the second paper
docker pull zjucapg/semgtf:latest or docker pull registry.cn-hangzhou.aliyuncs.com/semgtf/semgtf:latest
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Dataset
Eleven databases including Ninapro DB1-DB7, CapgMyo DBa-DBc and CSL-HDEMG can be used for training and test.
mkdir .cache # put NinaPro DB1 in .cache/ninapro-db1 or NinaPro DB7 in .cache/ninapro-db7 # put CapgMyo DB-a in .cache/dba or DB-b in .cache/dbb or DB-c in .cache/dbc # put CSL-HDEMG in .cache/csl
The NinaPro DB1 needs to be segmented by gesture labels and stored in Matlab format as follows.
.cache/ninapro-db1/data/sss/ggg/sss_ggg_ttt.mat
contains a fielddata
reprensents the trialttt
of gestureggg
of subjectsss
. And numbers start from zero. Gesture 0 is the rest gesture.For instance,
.cache/ninapro-db1/data/000/001/000_001_000.mat
is the 0th trial of 1st gesture of the 0th subject.You can download the original dataset from https://www.idiap.ch/project/ninapro/database or download the prepared dataset from our site http://zju-capg.org/myo/data/ninapro-db1.zip. CapgMyo and CSL-HDEMG datasets can be acquired on http://zju-capg.org/myo/data and http://www.csl.uni-bremen.de/cms/forschung/bewegungserkennung, respectively.
-
Quick Start
# Get into the capg/semg:mscnn container nvidia-docker run -ti -v your_projectdir:/code zjucapg/semg /bin/bash # first paper # Train sh scripts/exp.sh # Test python scripts/test.py # second paper # Train sh exp.sh # Test sh test.sh
Licensed under an GPL v3.0 license.
@article{hu2018novel,
title={A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition},
author={Hu, Yu and Wong, Yongkang and Wei, Wentao and Du, Yu and Kankanhalli, Mohan and Geng, Weidong},
journal={PloS one},
volume={13},
number={10},
pages={e0206049},
year={2018},
publisher={Public Library of Science}
}
@article{hu2019semg,
title={sEMG-Based Gesture Recognition With Embedded Virtual Hand Poses and Adversarial Learning},
author={Hu, Yu and Wong, Yongkang and Dai, Qingfeng and Kankanhalli, Mohan and Geng, Weidong and Li, Xiangdong},
journal={IEEE Access},
volume={7},
pages={104108--104120},
year={2019},
publisher={IEEE}
}