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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Vydáno v:2013 6th International Symposium on Resilient Control Systems (ISRCS) s. 74 - 79
Hlavní autoři: Potluri, Chandrasekhar, Anugolu, Madhavi, Schoen, Marco P., Naidu, D. Subbaram, Urfer, Alex, Rieger, Craig
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2013
Témata:
Algorithm design and analysis
Approximate Entropy
Computational modeling
Data fusion
Entropy
Force
Fuzzy logic
Muscles
Sensors
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Shrnutí: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.
DOI:10.1109/ISRCS.2013.6623754