TY - GEN
T1 - Feature Selection and Ranking in EMG Analysis for Hand Movement Classification
AU - Chandrika, Parvatam Ramya
AU - Powar, Omkar S.
AU - Chemmangat, Krishnan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Surface Electromyography has gained tremendous significance in the recent years due to its suitability and reliability in a wide range of applications like automatic prosthetic control, diagnosis of neuromuscular disorders, in robotics and many such fields. Considering such applications, identification of various muscular movements is necessary and hence, EMG pattern recognition is needed. This paper focusses on a generalised EMG pattern recognition of various hand movements. The data from Ninapro Database - 4 has been used for pattern recognition. The database has Surface Electromyogram (sEMG) data of 52 various hand movements. The data was subjected to pre-processing, feature extraction and classification. An average accuracy of 64.87% was obtained for a combination of seven features in the time (temporal) domain, using Linear Discriminant Analysis (LDA) as the classification model. The obtained classification accuracies are compared and discussed with respect to the state-of-the-art literature.
AB - Surface Electromyography has gained tremendous significance in the recent years due to its suitability and reliability in a wide range of applications like automatic prosthetic control, diagnosis of neuromuscular disorders, in robotics and many such fields. Considering such applications, identification of various muscular movements is necessary and hence, EMG pattern recognition is needed. This paper focusses on a generalised EMG pattern recognition of various hand movements. The data from Ninapro Database - 4 has been used for pattern recognition. The database has Surface Electromyogram (sEMG) data of 52 various hand movements. The data was subjected to pre-processing, feature extraction and classification. An average accuracy of 64.87% was obtained for a combination of seven features in the time (temporal) domain, using Linear Discriminant Analysis (LDA) as the classification model. The obtained classification accuracies are compared and discussed with respect to the state-of-the-art literature.
UR - https://www.scopus.com/pages/publications/85179517615
UR - https://www.scopus.com/inward/citedby.url?scp=85179517615&partnerID=8YFLogxK
U2 - 10.1109/TENCON58879.2023.10322317
DO - 10.1109/TENCON58879.2023.10322317
M3 - Conference contribution
AN - SCOPUS:85179517615
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 966
EP - 970
BT - TENCON 2023 - 2023 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th IEEE Region 10 Conference, TENCON 2023
Y2 - 31 October 2023 through 3 November 2023
ER -