EMG Signal Processing with Clustering Algorithms for motor gesture Tasks

Recent research shows the possibility of using electromyography (EMG) electrical signals to control devices or prosthesis. The EMG signals are measured in muscles, such as the forearm. These signals can lead to determine the intentionality of the patient when performing any motor tasks, however the...

Full description

Saved in:
Bibliographic Details
Published in:2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) pp. 1 - 6
Main Authors: Asanza, Victor, Pelaez, Enrique, Loayza, Francis, Mesa, Iker, Diaz, Javier, Valarezo, Edwin
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2018
Subjects:
Butterworth Filter
Clustering algorithms
DBSCAN
Electrodes
Electromyography
Fast Fourier Transform
Feature extraction
Gaussian distribution
Hierarchical-Clustering
K-means
Muscles
Task analysis
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Recent research shows the possibility of using electromyography (EMG) electrical signals to control devices or prosthesis. The EMG signals are measured in muscles, such as the forearm. These signals can lead to determine the intentionality of the patient when performing any motor tasks, however the signals are susceptible to noise due to the voltage sensed, which is in the microvolts scale. In this work, the preprocessing of the EMG signals includes the design and test of a filter. Our designed filter allows eliminating any signal components from the electrical network or any other sources that are not EMG signals. To validate the preprocessing efficiency, we analyze the frequency components and the distribution of the filtered EMG signals. Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms to determine a subject's intention when performing a task. The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, as compared to the DBSCAN and Hierarchical algorithms, which were not able to perform the clustering as expected. However, they match the performance of clustering two groups of combining gestures.
AbstractList Recent research shows the possibility of using electromyography (EMG) electrical signals to control devices or prosthesis. The EMG signals are measured in muscles, such as the forearm. These signals can lead to determine the intentionality of the patient when performing any motor tasks, however the signals are susceptible to noise due to the voltage sensed, which is in the microvolts scale. In this work, the preprocessing of the EMG signals includes the design and test of a filter. Our designed filter allows eliminating any signal components from the electrical network or any other sources that are not EMG signals. To validate the preprocessing efficiency, we analyze the frequency components and the distribution of the filtered EMG signals. Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms to determine a subject's intention when performing a task. The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, as compared to the DBSCAN and Hierarchical algorithms, which were not able to perform the clustering as expected. However, they match the performance of clustering two groups of combining gestures.
Author Pelaez, Enrique
Asanza, Victor
Diaz, Javier
Mesa, Iker
Loayza, Francis
Valarezo, Edwin
Author_xml – sequence: 1
  givenname: Victor
  surname: Asanza
  fullname: Asanza, Victor
  organization: Facultad de Ingeniería en Electricidad y, Computación, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
– sequence: 2
  givenname: Enrique
  surname: Pelaez
  fullname: Pelaez, Enrique
  organization: Facultad de Ingeniería en Electricidad y, Computación, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
– sequence: 3
  givenname: Francis
  surname: Loayza
  fullname: Loayza, Francis
  organization: Facultad de Ingeniería en Electricidad y, Computación, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
– sequence: 4
  givenname: Iker
  surname: Mesa
  fullname: Mesa, Iker
  organization: CEIT, Parque Tecnológico de San Sebastian, Universidad de Navarra, San Sebastian, Spain
– sequence: 5
  givenname: Javier
  surname: Diaz
  fullname: Diaz, Javier
  organization: CEIT, Parque Tecnológico de San Sebastian, Universidad de Navarra, San Sebastian, Spain
– sequence: 6
  givenname: Edwin
  surname: Valarezo
  fullname: Valarezo, Edwin
  organization: Department of Biomedical Engineering, Kyung Hee University, Yongin, Republic of Korea
BookMark eNotj9tKw0AYhFdQUNs8gHizL5C4591cllBboUWhuS9_kz9xNQfZTRHf3oi9mWHmYvjmnlwP44CEPHCWcc7yp3VZ7DPBuMucdkxYdkWS3DqupTPGaJvfkiTGD8aYME5xLu_Idr3f0INvB-joWxgrjNEPLf320zstunOcMPzlVdeOYe76SJsx0H6cZm0xTueAtIT4GZfkpoEuYnLxBSmfZ55tunvdvBSrXepzNqXgnLJcVNDY2ohKI0olEUArONVNUwHgzC9NrS3YEzKNyinphMRaOSlBLsjj_6xHxONX8D2En-PlrvwF-T5Nbg
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ETCM.2018.8580270
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781538666579
153866657X
EndPage 6
ExternalDocumentID 8580270
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAJGR
AAWTH
ABLEC
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i90t-a884712caf7d62c5ee343eaa54abdffcaae20136d57a7be05e4843823ed4833a3
IEDL.DBID RIE
IngestDate Wed Aug 27 02:53:37 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i90t-a884712caf7d62c5ee343eaa54abdffcaae20136d57a7be05e4843823ed4833a3
PageCount 6
ParticipantIDs ieee_primary_8580270
PublicationCentury 2000
PublicationDate 2018-Oct.
PublicationDateYYYYMMDD 2018-10-01
PublicationDate_xml – month: 10
  year: 2018
  text: 2018-Oct.
PublicationDecade 2010
PublicationTitle 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)
PublicationTitleAbbrev ETCM
PublicationYear 2018
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0002684113
Score 1.6940205
Snippet Recent research shows the possibility of using electromyography (EMG) electrical signals to control devices or prosthesis. The EMG signals are measured in...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Butterworth Filter
Clustering algorithms
DBSCAN
Electrodes
Electromyography
Fast Fourier Transform
Feature extraction
Gaussian distribution
Hierarchical-Clustering
K-means
Muscles
Task analysis
Title EMG Signal Processing with Clustering Algorithms for motor gesture Tasks
URI https://ieeexplore.ieee.org/document/8580270
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8IwFG-QePCkBozf6cGjhY22W3s0BOUCIZEDN_LWviERwTDm32_fWDAmXrws3ZZm23tt3n7v9z4YewCdgreJFZnxmVDeobB5rARoaTU4S9xP1WwiHY_NbGYnDfZ4yIVBxCr4DDs0rLh8v3Elucq6RpuAogJAP0rTZJ-rdfCnUNWSOJY1cRlHtjuY9kcUu2U69bxfDVQq-_F8-r8nn7H2TyIenxxMzDlr4LrFhoPRC39dLsIS4HWcf7jJyaPK-6uSKh_Q-dNqsQnI_-2j4OHHlAeVhCOxSeUW-RSK96LNps_h9YeiboggljbaCTBkSnoO8tQnPacRpZIIoBVkPs8dAPaoBJsP8k8zjDQqU_F86JWREuQFa643a7xkPMtzCEAs7GcVKZAJGJuYJEJjQFrw8RVrkRDmn_uSF_P6-6__vnzDTkjO-xi3W9bcbUu8Y8fua7cstveVnr4BhbeVOQ
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8IwFG8ImuhJDRi_7cGjg21tt_ZoCIgRCIk7cCNv7RsS-TCM-ffbjgVj4sXL0m1ptr3X5u33fu-DkAcQMRgVKS-VJvW40eipLOAeCKYEaOW4n7LZRDwayclEjWvkcZ8Lg4hl8Bm23LDk8s1aF85V1pZCWhRlAfqB4Dz0d9lae4-Kq1sSBKyiLgNftbtJZ-iit2SrmvmrhUppQXon_3v2KWn-pOLR8d7InJEarhqk3x0-07f5zC4CWkX625vU-VRpZ1G42gfu_GkxW1vs_77Mqf01pVYp9uj4pGKDNIH8I2-SpGdfv-9VLRG8ufK3HkhnTEINWWyiUAtExhkCCA6pyTINgKErwmasBuIUfYFclkwfGi4ZA3ZO6qv1Ci8ITbMMLBSzO5r7HFgEUkUy8lFKYApMcEkaTgjTz13Ri2n1_Vd_X74nR_1kOJgOXkav1-TYyXwX8XZD6ttNgbfkUH9t5_nmrtTZN3mzmIA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2018+IEEE+Third+Ecuador+Technical+Chapters+Meeting+%28ETCM%29&rft.atitle=EMG+Signal+Processing+with+Clustering+Algorithms+for+motor+gesture+Tasks&rft.au=Asanza%2C+Victor&rft.au=Pelaez%2C+Enrique&rft.au=Loayza%2C+Francis&rft.au=Mesa%2C+Iker&rft.date=2018-10-01&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FETCM.2018.8580270&rft.externalDocID=8580270