Computational intelligence based data fusion algorithm for dynamic sEMG and skeletal muscle force modelling

In this work, an array of three surface Electromyography (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 signa...

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Published in:2013 6th International Symposium on Resilient Control Systems (ISRCS) pp. 74 - 79
Main Authors: Potluri, Chandrasekhar, Anugolu, Madhavi, Schoen, Marco P., Naidu, D. Subbaram, Urfer, Alex, Rieger, Craig
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2013
Subjects:
Algorithm design and analysis
Approximate Entropy
Computational modeling
Data fusion
Entropy
Force
Fuzzy logic
Muscles
Sensors
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Abstract In this work, an array of three surface Electromyography (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.
AbstractList In this work, an array of three surface Electromyography (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.
Author Potluri, Chandrasekhar
Naidu, D. Subbaram
Rieger, Craig
Schoen, Marco P.
Urfer, Alex
Anugolu, Madhavi
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  givenname: Madhavi
  surname: Anugolu
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  givenname: Marco P.
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  givenname: D. Subbaram
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  givenname: Craig
  surname: Rieger
  fullname: Rieger, Craig
  email: craig.rieger@inl.gov
  organization: Idaho Nat. Lab., Idaho Falls, ID, USA
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Snippet In this work, an array of three surface Electromyography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test...
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StartPage 74
SubjectTerms Algorithm design and analysis
Approximate Entropy
Computational modeling
Data fusion
Entropy
Force
Fuzzy logic
Muscles
Sensors
Title Computational intelligence based data fusion algorithm for dynamic sEMG and skeletal muscle force modelling
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