TY - GEN
T1 - A New Methodology for Pattern Recognition Applied to Hand Gestures Recognition Using EMG. Analysis of Intrapersonal and Interpersonal Variability
AU - Ordóñez Flores, Javier Alejandro
AU - Alvarez Rueda, Robin Gerardo
AU - Benalcázar, Marco E.
AU - Barona López, Lorena Isabel
AU - Leonardo, Ángel
AU - Cruz, Patricio
AU - Vásconez, Juan Pablo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/12
Y1 - 2021/10/12
N2 - Systems used for pattern recognition are usually divided into 4 stages: signal acquisition, preprocessing, feature extraction and classification. However, the use of algorithms in these last 3 stages is not justified and researchers use them without criteria other than the result achieved at the end of the process. In this paper we propose a new methodology and show its particular application to the recognition of five hand gestures based on 8 channels of Electromyography using the Myo armband device placed on the forearm. If n features are extracted, they will form clusters of points in n-dimensional space and now the selection of the best preprocessing algorithms and the best features are based on maximizing the distance among clusters. On the other hand, both intrapersonal and interpersonal variability are treated to facilitate the understanding of the phenomenon. As a demonstration, it was applied to 12 people and the recognition accuracy was 97%.
AB - Systems used for pattern recognition are usually divided into 4 stages: signal acquisition, preprocessing, feature extraction and classification. However, the use of algorithms in these last 3 stages is not justified and researchers use them without criteria other than the result achieved at the end of the process. In this paper we propose a new methodology and show its particular application to the recognition of five hand gestures based on 8 channels of Electromyography using the Myo armband device placed on the forearm. If n features are extracted, they will form clusters of points in n-dimensional space and now the selection of the best preprocessing algorithms and the best features are based on maximizing the distance among clusters. On the other hand, both intrapersonal and interpersonal variability are treated to facilitate the understanding of the phenomenon. As a demonstration, it was applied to 12 people and the recognition accuracy was 97%.
KW - EMG
KW - hand gesture recognition
KW - intrapersonal and interpersonal variability
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85119412258&partnerID=8YFLogxK
U2 - 10.1109/ETCM53643.2021.9590695
DO - 10.1109/ETCM53643.2021.9590695
M3 - Conference contribution
AN - SCOPUS:85119412258
T3 - ETCM 2021 - 5th Ecuador Technical Chapters Meeting
BT - ETCM 2021 - 5th Ecuador Technical Chapters Meeting
A2 - Huerta, Monica Karel
A2 - Quevedo, Sebastian
A2 - Monsalve, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021
Y2 - 12 October 2021 through 15 October 2021
ER -