A User Independent Method for Identifying Hand Gestures with sEMG

Hitoshi Tamura, Kazuki Itou, Yasushi Kambayashi

2020

Abstract

We propose a method to determine hand gestures using sEMG (surface Electromyogram) measured from the forearm. The detection method uses the LSTM (Long Short Term Memory) model of RNN (Recurrent Neural Network). Although the conventional method requires the learning data of the user, this is a method that an unspecified number of users can use immediately by enhancing the data. We have confirmed that the accuracy does not change even if the mounting position of the sensor is shifted. We have shown the effectiveness of the data enhancement by numerical experiments.

Paper Citation

in Harvard Style

Tamura H., Itou K. and Kambayashi Y. (2020). A User Independent Method for Identifying Hand Gestures with sEMG. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT, ISBN 978-989-758-395-7, pages 355-362. DOI: 10.5220/0009370103550362

in Bibtex Style

@conference{hamt20,
author={Hitoshi Tamura and Kazuki Itou and Yasushi Kambayashi},
title={A User Independent Method for Identifying Hand Gestures with sEMG},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT,},
year={2020},
pages={355-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009370103550362},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT,
TI - A User Independent Method for Identifying Hand Gestures with sEMG
SN - 978-989-758-395-7
AU - Tamura H.
AU - Itou K.
AU - Kambayashi Y.
PY - 2020
SP - 355
EP - 362
DO - 10.5220/0009370103550362