Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis

Simon A. Stuttaford; Agamemnon Krasoulis; Sigrid S.G. Dupan; Kianoush Nazarpour; Matthew Dyson

doi:10.1109/EMBC44109.2020.9175614

Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis

Simon A. Stuttaford, Agamemnon Krasoulis, Sigrid S.G. Dupan, Kianoush Nazarpour, Matthew Dyson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period. Two variations of principal component analysis (PCA) were applied to electromyography (EMG) data collected during a myoelectric task. Candid covariance-free incremental PCA (CCIPCA) detected task-specific muscle synergies with high accuracy using minimal amounts of data. Our findings offer a real-time solution towards optimizing calibration periods.

Original language	English
Title of host publication	2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3497-3500
Number of pages	4
ISBN (Electronic)	978-1-7281-1990-8
ISBN (Print)	978-1-7281-1991-5
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175614
Publication status	Published - 27 Aug 2020
Event	42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Montréal, Québec, Canada Duration: 20 Jul 2020 → 24 Jul 2020 Conference number: 42 https://embc.embs.org/2020/

Publication series

Name
Publisher	IEEE
ISSN (Print)	1557-170X
ISSN (Electronic)	1558-4615

Conference

Conference	42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated title	EMBC 2020
Country/Territory	Canada
City	Québec
Period	20/07/20 → 24/07/20
Internet address	https://embc.embs.org/2020/

Access to Document

10.1109/EMBC44109.2020.9175614Licence: All Rights Reserved

https://ieeexplore.ieee.org/abstract/document/9175614Licence: All Rights Reserved

Sensorimotor learning for control of prosthetic limbs
Nazarpour, K.
1/02/18 → 31/01/23
Project: Research

Cite this

Stuttaford, S. A., Krasoulis, A., Dupan, S. S. G., Nazarpour, K., & Dyson, M. (2020). Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC) (pp. 3497-3500). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC44109.2020.9175614

@inproceedings{035f12d5feea444083a5adf73f954708,

title = "Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis",

abstract = "The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period. Two variations of principal component analysis (PCA) were applied to electromyography (EMG) data collected during a myoelectric task. Candid covariance-free incremental PCA (CCIPCA) detected task-specific muscle synergies with high accuracy using minimal amounts of data. Our findings offer a real-time solution towards optimizing calibration periods.",

author = "Stuttaford, {Simon A.} and Agamemnon Krasoulis and Dupan, {Sigrid S.G.} and Kianoush Nazarpour and Matthew Dyson",

year = "2020",

month = aug,

day = "27",

doi = "10.1109/EMBC44109.2020.9175614",

language = "English",

isbn = "978-1-7281-1991-5",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "3497--3500",

booktitle = "2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)",

address = "United States",

note = "42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

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Stuttaford, SA, Krasoulis, A, Dupan, SSG, Nazarpour, K & Dyson, M 2020, Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), pp. 3497-3500, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Québec, Quebec, Canada, 20/07/20. https://doi.org/10.1109/EMBC44109.2020.9175614

Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis. / Stuttaford, Simon A.; Krasoulis, Agamemnon; Dupan, Sigrid S.G. et al.

2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 3497-3500.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis

AU - Stuttaford, Simon A.

AU - Krasoulis, Agamemnon

AU - Dupan, Sigrid S.G.

AU - Nazarpour, Kianoush

AU - Dyson, Matthew

N1 - Conference code: 42

PY - 2020/8/27

Y1 - 2020/8/27

N2 - The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period. Two variations of principal component analysis (PCA) were applied to electromyography (EMG) data collected during a myoelectric task. Candid covariance-free incremental PCA (CCIPCA) detected task-specific muscle synergies with high accuracy using minimal amounts of data. Our findings offer a real-time solution towards optimizing calibration periods.

AB - The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period. Two variations of principal component analysis (PCA) were applied to electromyography (EMG) data collected during a myoelectric task. Candid covariance-free incremental PCA (CCIPCA) detected task-specific muscle synergies with high accuracy using minimal amounts of data. Our findings offer a real-time solution towards optimizing calibration periods.

U2 - 10.1109/EMBC44109.2020.9175614

DO - 10.1109/EMBC44109.2020.9175614

M3 - Conference contribution

SN - 978-1-7281-1991-5

SP - 3497

EP - 3500

BT - 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 20 July 2020 through 24 July 2020

ER -

Stuttaford SA, Krasoulis A, Dupan SSG, Nazarpour K, Dyson M. Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Institute of Electrical and Electronics Engineers (IEEE). 2020. p. 3497-3500 Epub 2020 Jul 20. doi: 10.1109/EMBC44109.2020.9175614

Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis

Abstract

Publication series

Conference

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Projects

Sensorimotor learning for control of prosthetic limbs

Cite this