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Authors: Tara Baldacchino ; William Jacobs ; Sean R. Anderson ; Keith Worden and Jennifer Rowson

Affiliation: University of Sheffield, United Kingdom

ISBN: 978-989-758-069-7

Keyword(s): sEMG Signals, Finger Force Regression, Variational Bayes, Multivariate Mixture of Experts, Prosthetic Hand.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Soft Computing

Abstract: Improving the dexterity of active prostheses is a major research area amalgamating machine learning algorithms and biosignals. A recent research niche has emerged from this- providing proportional control to a prosthetic hand by modelling the force applied at the fingertips using surface electromyography (sEMG). The publicly released NinaPro database contains sEMG recording for 6 degree-of-freedom force activations for 40 intact subjects. In this preliminary study the authors successfully perform multivariate force regression using Bayesian mixture of experts (MoE). The accuracy of the model is compared to the benchmark set by the authors of NinaPro; comparable performance is achieved, however in this work a lower dimensional feature extraction representation obtains the best modelling accuracies, hence reducing training time. Inherent to the Bayesian framework is the inclusion of uncertainty in the model structure, providing a natural step in obtaining confidence bounds on the predic tions. The MoE model used in this paper provides a powerful method for modelling force regression with application to actively controlling prosthetic and robotic arms for rehabilitation purposes, resulting in highly refined movements. (More)

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Paper citation in several formats:
Baldacchino, T.; Jacobs, W.; Anderson, S.; Worden, K. and Rowson, J. (2015). Estimation of Fingertip Force from Surface EMG - A Multivariate Bayesian Mixture of Experts Approach.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 270-276. DOI: 10.5220/0005260402700276

@conference{biosignals15,
author={Tara Baldacchino. and William Jacobs. and Sean R. Anderson. and Keith Worden. and Jennifer Rowson.},
title={Estimation of Fingertip Force from Surface EMG - A Multivariate Bayesian Mixture of Experts Approach},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={270-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005260402700276},
isbn={978-989-758-069-7},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Estimation of Fingertip Force from Surface EMG - A Multivariate Bayesian Mixture of Experts Approach
SN - 978-989-758-069-7
AU - Baldacchino, T.
AU - Jacobs, W.
AU - Anderson, S.
AU - Worden, K.
AU - Rowson, J.
PY - 2015
SP - 270
EP - 276
DO - 10.5220/0005260402700276

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