TY - GEN
T1 - Computational intelligence based data fusion algorithm for dynamic sEMG and skeletal muscle force modelling
AU - Potluri, Chandrasekhar
AU - Anugolu, Madhavi
AU - Schoen, Marco P.
AU - Naidu, D. Subbaram
AU - Urfer, Alex
AU - Rieger, Craig
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusion-based approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.
AB - In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusion-based approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.
KW - Approximate Entropy
KW - Data fusion
KW - Fuzzy logic
UR - http://www.scopus.com/inward/record.url?scp=84890033507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890033507&partnerID=8YFLogxK
U2 - 10.1109/ISRCS.2013.6623754
DO - 10.1109/ISRCS.2013.6623754
M3 - Conference contribution
AN - SCOPUS:84890033507
SN - 9781479905034
T3 - Proceedings - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013
SP - 74
EP - 79
BT - Proceedings - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013
PB - IEEE Computer Society
T2 - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013
Y2 - 13 August 2013 through 15 August 2013
ER -