- A CUDA compatible GPU
- Ubuntu 14.04 or any other Linux/Unix that can run Docker
- Docker
- Nvidia Docker
- Download the docker image:
or build it by yourself:
docker pull answeror/sigr:semidocker build -t answeror/sigr:semi -f docker/Dockerfile .
# Prepare data
mkdir -p .cache
# Download https://www.idiap.ch/project/ninapro
# Put NinaPro DB1 in .cache/ninapro-db1-raw
# Download http://zju-capg.org/myo/data
# Put CapgMyo DB-a in .cache/dba
# Put CapgMyo DB-b in .cache/dbb
# Put CapgMyo DB-c in .cache/dbc
# Download http://www.csl.uni-bremen.de/cms/forschung/bewegungserkennung
# Put csl-hdemg in .cache/csl
scripts/train_table_4.sh
scripts/app test_semimyo --cmd table_4
Training on NinaPro and CapgMyo will take 1 to 2 hours depending on your GPU.
Training on csl-hdemg will take several days.
You can accelerate traning and testing by distribute different folds on different GPUs with the gpu parameter.
You can also do train and test for each dataset on different machines or GPUs:
scripts/train_db1.sh
scripts/train_dba.sh
scripts/train_dbb.sh
scripts/train_dbc.sh
scripts/train_csl.sh
scripts/app test_semimyo --cmd table_4_db1
scripts/app test_semimyo --cmd table_4_dba
scripts/app test_semimyo --cmd table_4_dbb
scripts/app test_semimyo --cmd table_4_dbc
scripts/app test_semimyo --cmd table_4_csl
# Prepare data
mkdir -p .cache
# Download https://www.idiap.ch/project/ninapro
# Put NinaPro DB1 in .cache/ninapro-db1-raw
# Extract pre-calculated cluster (512 clusters) labels to data folder
unzip data/pose-512 -d .cache/ninapro-db1-raw
scripts/train_table_1.sh
scripts/app test_semimyo --cmd table_1
Licensed under an GPL v3.0 license.
@inproceedings{Du_IJCAI_2017,
author = {Yu Du, Yongkang Wong, Wenguang Jin, Wentao Wei, Yu Hu, Mohan Kankanhalli, Weidong Geng},
title = {Semi-Supervised Learning for Surface EMG-based Gesture Recognition},
booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence, {IJCAI-17}},
pages = {1624--1630},
year = {2017},
doi = {10.24963/ijcai.2017/225},
url = {https://doi.org/10.24963/ijcai.2017/225},
}
Thanks DMLC team for their great MxNet!
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