Improved Gesture Recognition based on sEMG Signals and TCN

This is the code accompaniment for the following paper presented at ICASSP 2019:
P. Tsinganos et al., “Improved Gesture Recognition Based on sEMG Signals and TCN,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 1169–1173.

Requirements

The following python packages are needed to run the code:

• keras 2.2.4 (from tensorflow library)
• tensorflow 1.13.1
• sklearn 0.20.3
• scipy 1.2.1
• numpy 1.16.2

Usage

To replicate the experiments described in the paper run: bash run.sh. Before running the code download the Ninapro dataset as described in the dataset folder.

License

If this code helps your research, please cite the paper.

@inproceedings{Tsinganos2019,
address = {Brighton, UK},
author = {Tsinganos, Panagiotis and Cornelis, Bruno and Cornelis, Jan and Jansen, Bart and Skodras, Athanassios},
booktitle = {ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/ICASSP.2019.8683239},
month = {may},
pages = {1169--1173},
publisher = {IEEE},
title = {{Improved Gesture Recognition Based on sEMG Signals and TCN}},
year = {2019}
}

Acknowledgements

The work is supported by the "Andreas Mentzelopoulos Scholarships for the University of Patras" and the VUB-UPatras International Joint Research Group on ICT (JICT).

Contact Details

Panagiotis Tsinganos | PhD Candidate
University of Patras, Greece
Vrije Universiteit Brussel, Belgium
panagiotis.tsinganos@ece.upatras.gr