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

Chandrasekhar Potluri, Madhavi Anugolu, Marco P. Schoen, D. Subbaram Naidu, Alex Urfer, Craig Rieger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013
PublisherIEEE Computer Society
Pages74-79
Number of pages6
ISBN (Print)9781479905034
DOIs
StatePublished - Jan 1 2013
Event2013 6th International Symposium on Resilient Control Systems, ISRCS 2013 - San Francisco, CA, United States
Duration: Aug 13 2013Aug 15 2013

Publication series

NameProceedings - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013

Other

Other2013 6th International Symposium on Resilient Control Systems, ISRCS 2013
CountryUnited States
CitySan Francisco, CA
Period8/13/138/15/13

    Fingerprint

Keywords

  • Approximate Entropy
  • Data fusion
  • Fuzzy logic

Cite this

Potluri, C., Anugolu, M., Schoen, M. P., Naidu, D. S., Urfer, A., & Rieger, C. (2013). Computational intelligence based data fusion algorithm for dynamic sEMG and skeletal muscle force modelling. In Proceedings - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013 (pp. 74-79). [6623754] (Proceedings - 2013 6th International Symposium on Resilient Control Systems, ISRCS 2013). IEEE Computer Society. https://doi.org/10.1109/ISRCS.2013.6623754