Sensors paper

Author: Answeror

.dockerignore

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+.cache/

.gitignore

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+*.swp
+*.pyc
+.ipynb_checkpoints/
+.cache/
+/scripts/exp_inter
+/tmp/

LICENSE

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+# Surface EMG-based Inter-session Gesture Recognition Enhanced by Deep Domain Adaptation
+
+## Requirements
+
+* A CUDA compatible GPU
+* Ubuntu 14.04 or any other Linux/Unix that can run Docker
+* [Docker](http://docker.io/)
+* [Nvidia Docker](https://github.com/NVIDIA/nvidia-docker)
+
+## Usage
+
+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.
+
+## License
+
+Licensed under an GPL v3.0 license.
+
+## Misc
+
+Thanks DMLC team for their great [MxNet](https://github.com/dmlc/mxnet)!

README.md

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+FROM answeror/mxnet:f2684a6
+MAINTAINER answeror <[email protected]>
+
+RUN apt-get install -y python-pip python-scipy
+RUN pip install click logbook joblib nose
+
+RUN cd /mxnet && \
+  git reset --hard && \
+  git checkout master && \
+  git pull
+
+RUN cd /mxnet && \
+  git checkout 7a485bb && \
+  git submodule update && \
+  git checkout 887491d src/operator/elementwise_binary_broadcast_op-inl.h && \
+  sed -i -e 's/CHECK(ksize_x <= dshape\[3\] && ksize_y <= dshape\[2\])/CHECK(ksize_x <= dshape[3] + 2 * param_.pad[1] \&\& ksize_y <= dshape[2] + 2 * param_.pad[0])/' src/operator/convolution-inl.h && \
+  cp make/config.mk . && \
+  echo "USE_CUDA=1" >>config.mk && \
+  echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk && \
+  echo "USE_CUDNN=1" >>config.mk && \
+  echo "USE_BLAS=openblas" >>config.mk && \
+  make clean && \
+  make -j8 ADD_LDFLAGS=-L/usr/local/cuda/lib64/stubs
+
+ADD elementwise_binary_broadcast_op-inl.h /mxnet/src/operator/elementwise_binary_broadcast_op-inl.h
+RUN cd /mxnet && \
+  make clean && \
+  make -j8 ADD_LDFLAGS=-L/usr/local/cuda/lib64/stubs
+
+RUN pip install jupyter pandas matplotlib seaborn scikit-learn
+RUN mkdir -p -m 700 /root/.jupyter/ && \
+  echo "c.NotebookApp.ip = '*'" >> /root/.jupyter/jupyter_notebook_config.py
+EXPOSE 8888
+CMD ["sh", "-c", "jupyter notebook"]
+
+WORKDIR /code