Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps

Elmer R. Escandon; Christian Flores

doi:10.1109/EIRCON51178.2020.9253761

Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps

Elmer R. Escandon, Christian Flores

Department of Electrical and Mechatronics Engineering

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

2 Scopus citations

Abstract

Recent advances in hand classification using noninvasive sensors permit the adequately recognition of movements with high precision. However, these applications in prosthesis are far from reality, since the acquired muscle signals does not meet real-life conditions. As recent databases incorporate these real conditions into their data acquisition protocol, it is necessary to analyze the muscle signal characteristics and evaluate if these could be separated. This paper applies the Higuchi's fractal dimension in two activities of daily living using real-life signals of the triceps brachii from the NinaPro database. The characteristics are first obtained from a feature extraction technique, then clustered using a two-level approach of k-means in a self-organizing map (SOM). The results from intra-subject analysis in 15 individuals show clusterization of the fractal dimension for sEMG signals using three Kmax values. The clusters selection are analyzed using a cluster score based on a similarity index for task identification.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728183671
DOIs	https://doi.org/10.1109/EIRCON51178.2020.9253761
State	Published - 21 Oct 2020
Event	2020 IEEE Engineering International Research Conference, EIRCON 2020 - Lima, Peru Duration: 21 Oct 2020 → 23 Oct 2020

Publication series

Name	Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020

Conference

Conference	2020 IEEE Engineering International Research Conference, EIRCON 2020
Country/Territory	Peru
City	Lima
Period	21/10/20 → 23/10/20

Keywords

Fractal Dimension
Self-Organizing map
k-means
real-life signals
triceps brachii

Access to Document

10.1109/EIRCON51178.2020.9253761

Cite this

Escandon, E. R., & Flores, C. (2020). Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps. In Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020 Article 9253761 (Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EIRCON51178.2020.9253761

Escandon, Elmer R. ; Flores, Christian. / Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps. Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020).

@inproceedings{3055c07e8a804a4bb3af339a977848ea,

title = "Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps",

abstract = "Recent advances in hand classification using noninvasive sensors permit the adequately recognition of movements with high precision. However, these applications in prosthesis are far from reality, since the acquired muscle signals does not meet real-life conditions. As recent databases incorporate these real conditions into their data acquisition protocol, it is necessary to analyze the muscle signal characteristics and evaluate if these could be separated. This paper applies the Higuchi's fractal dimension in two activities of daily living using real-life signals of the triceps brachii from the NinaPro database. The characteristics are first obtained from a feature extraction technique, then clustered using a two-level approach of k-means in a self-organizing map (SOM). The results from intra-subject analysis in 15 individuals show clusterization of the fractal dimension for sEMG signals using three Kmax values. The clusters selection are analyzed using a cluster score based on a similarity index for task identification.",

keywords = "Fractal Dimension, Self-Organizing map, k-means, real-life signals, triceps brachii",

author = "Escandon, {Elmer R.} and Christian Flores",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Engineering International Research Conference, EIRCON 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

year = "2020",

month = oct,

day = "21",

doi = "10.1109/EIRCON51178.2020.9253761",

language = "English",

series = "Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020",

}

Escandon, ER & Flores, C 2020, Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps. in Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020., 9253761, Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Engineering International Research Conference, EIRCON 2020, Lima, Peru, 21/10/20. https://doi.org/10.1109/EIRCON51178.2020.9253761

Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps. / Escandon, Elmer R.; Flores, Christian.
Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9253761 (Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020).

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

TY - GEN

T1 - Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps

AU - Escandon, Elmer R.

AU - Flores, Christian

PY - 2020/10/21

Y1 - 2020/10/21

N2 - Recent advances in hand classification using noninvasive sensors permit the adequately recognition of movements with high precision. However, these applications in prosthesis are far from reality, since the acquired muscle signals does not meet real-life conditions. As recent databases incorporate these real conditions into their data acquisition protocol, it is necessary to analyze the muscle signal characteristics and evaluate if these could be separated. This paper applies the Higuchi's fractal dimension in two activities of daily living using real-life signals of the triceps brachii from the NinaPro database. The characteristics are first obtained from a feature extraction technique, then clustered using a two-level approach of k-means in a self-organizing map (SOM). The results from intra-subject analysis in 15 individuals show clusterization of the fractal dimension for sEMG signals using three Kmax values. The clusters selection are analyzed using a cluster score based on a similarity index for task identification.

AB - Recent advances in hand classification using noninvasive sensors permit the adequately recognition of movements with high precision. However, these applications in prosthesis are far from reality, since the acquired muscle signals does not meet real-life conditions. As recent databases incorporate these real conditions into their data acquisition protocol, it is necessary to analyze the muscle signal characteristics and evaluate if these could be separated. This paper applies the Higuchi's fractal dimension in two activities of daily living using real-life signals of the triceps brachii from the NinaPro database. The characteristics are first obtained from a feature extraction technique, then clustered using a two-level approach of k-means in a self-organizing map (SOM). The results from intra-subject analysis in 15 individuals show clusterization of the fractal dimension for sEMG signals using three Kmax values. The clusters selection are analyzed using a cluster score based on a similarity index for task identification.

KW - Fractal Dimension

KW - Self-Organizing map

KW - k-means

KW - real-life signals

KW - triceps brachii

UR - http://www.scopus.com/inward/record.url?scp=85097826811&partnerID=8YFLogxK

U2 - 10.1109/EIRCON51178.2020.9253761

DO - 10.1109/EIRCON51178.2020.9253761

M3 - Conference contribution

AN - SCOPUS:85097826811

T3 - Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020

BT - Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE Engineering International Research Conference, EIRCON 2020

Y2 - 21 October 2020 through 23 October 2020

ER -

Escandon ER, Flores C. Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps. In Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9253761. (Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020). doi: 10.1109/EIRCON51178.2020.9253761

Clustering of sEMG signals on real-life activities using fractal dimension and self-organizing maps

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this