• A CUDA compatible GPU
• Ubuntu 14.04 or any other Linux/Unix that can run Docker
• Docker
• Nvidia Docker

Following commands will (1) pull docker image (see docker/Dockerfile for details); (2) train ConvNets on the training sets of CSL-HDEMG, CapgMyo and NinaPro DB1, respectively; and (3) test trained ConvNets on the test sets.

mkdir .cache
# put NinaPro DB1 in .cache/ninapro-db1
# put CapgMyo DB-a in .cache/dba
# put CapgMyo DB-b in .cache/dbb
# put CapgMyo DB-c in .cache/dbc
# put CSL-HDEMG in .cache/csl
docker pull answeror/sigr:2016-09-21
scripts/train.sh
scripts/test.sh

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.

The NinaPro DB1 should be segmented according to the gesture labels and stored in Matlab format as follows. .cache/ninapro-db1/data/sss/ggg/sss_ggg_ttt.mat contains a field data (frames x channels) represents the trial ttt of gesture ggg of subject sss. Numbers are starting from zero. Gesture 0 is the rest posture. For example, .cache/ninapro-db1/data/000/001/000_001_000.mat is the 0th trial of 1st gesture of 0th subject, and .cache/ninapro-db1/data/002/003/002_003_004.mat is the 4th trial of 3th gesture of 2nd subject. You can download the prepared dataset from http://zju-capg.org/myo/data/ninapro-db1.zip or prepare it by yourself.

Licensed under an GPL v3.0 license.

@article{Du_Sensors_2017,
  title={{Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation}},
  author={Du, Yu and Jin, Wenguang and Wei, Wentao and Hu, Yu and Geng, Weidong},
  journal={Sensors},
  volume={17},
  number={3},
  pages={458},
  year={2017},
  publisher={Multidisciplinary Digital Publishing Institute}
}

Thanks DMLC team for their great MxNet!