Identifying Electromyography Sensor Placement using Dense Neural Networks

Paolo Cappellari, Robert Gaunt, Carl Beringer, Misagh Mansouri, Massimiliano Novelli

2018

Abstract

Neural networks are increasingly being used in medical settings to support medical practitioners and researchers in performing their work. In the field of prosthetics for amputees, sensors can be used to monitor the activity of remaining muscle and ultimately control prosthetic limbs. In this work, we present an approach to identify the location of intramuscular electromyograph sensors percutaneously implanted in extrinsic muscles of the forearm controlling the fingers and wrist during single digit movements. A major challenge is to confirm whether each sensor is placed in the targeted muscle, as this information can be critical in developing and implementing control systems for prosthetic limbs. We propose an automated approach, based on artificial neural networks, to identify the correct placement of an individual sensor. Our approach can provide feedback on each placed sensor, so researchers can validate the source of each signal before performing their data analysis.

Paper Citation

in Harvard Style

Cappellari P., Gaunt R., Beringer C., Mansouri M. and Novelli M. (2018). Identifying Electromyography Sensor Placement using Dense Neural Networks.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 130-141. DOI: 10.5220/0006912501300141

in Bibtex Style

@conference{data18,
author={Paolo Cappellari and Robert Gaunt and Carl Beringer and Misagh Mansouri and Massimiliano Novelli},
title={Identifying Electromyography Sensor Placement using Dense Neural Networks},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={130-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006912501300141},
isbn={978-989-758-318-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Identifying Electromyography Sensor Placement using Dense Neural Networks
SN - 978-989-758-318-6
AU - Cappellari P.
AU - Gaunt R.
AU - Beringer C.
AU - Mansouri M.
AU - Novelli M.
PY - 2018
SP - 130
EP - 141
DO - 10.5220/0006912501300141