Work in progress.
LongTerm 3DC Dataset is available here: http://ieee-dataport.org/1948
3DC Dataset is available here: https://github.com/UlysseCoteAllard/sEMG_handCraftedVsLearnedFeatures
First prepare the longterm 3DC Dataset by running: PrepareAndLoadDataLongTerm->prepare_from_raw_dataset.py Then in TrainingsAndEvaluations all the files are there which were used to obtain the results from: Virtual Reality to Study the Gap Between Offline and Real-Time EMG-based Gesture Recognition https://arxiv.org/abs/1912.09380
And
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition
In TrainingsAndEvaluations->ForTrainingSessions you have the different mains to train the algorithms (spectrograms refers to the Unsupervised Domain Adversarial paper, otherwise it's the Virtual Reality paper). In the TrainingsAndEvaluations->self_learning you have the files to train both SCADANN and MV. Then TrainingsAndEvaluations->ForEvaluationSessions are the files using the evaluation sessions for the Virtual Reality paper. TrainingsAndEvaluations->SpectrogramEvaluationSessions are the files using the evaluation sessions for the Unsupervised Domain Adversarial paper.
#Required libraries:
The VADA and Dirt-T implementation is based on: https://github.com/ozanciga/dirt-t and https://github.com/RuiShu/dirt-t
Numpy https://numpy.org/
SciPy https://www.scipy.org/
Scikit-learn http://scikit-learn.org/stable/
Sampen https://pypi.org/project/sampen/
PyWavelets https://pywavelets.readthedocs.io/en/latest/
Matplotlib https://matplotlib.org/
Pytorch https://pytorch.org/