Neural drive estimation using the hypothesis of muscle synergies and the state-constrained Kalman filter

Ghulam Rasool, Kamran Iqbal, Nidhal Bouaynaya, Gannon White

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

6 Scopus citations

Abstract

We explore the hypothesis of muscle synergies to estimate the neural drive (movement intent) for upper extremity myoelectric prosthesis using the surface myoelectric signals. Commonly employed pattern classification systems have certain limitations, like inherent discrete nature, finite movement classes and limited degrees-of-freedom. We propose a novel framework based on the state space modeling and the hypothesis of muscle synergies. The problem is formulated in the state space framework in a novel way, where the movement intent is modeled as the hidden state of the system. A continuous stream of the movement intent (the hidden state) is estimated using the state-constrained Kalman filter. Preliminary experimental results also confirm the applicability of the proposed framework for estimation of movement intent.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages802-805
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

Cite this

Rasool, G., Iqbal, K., Bouaynaya, N., & White, G. (2013). Neural drive estimation using the hypothesis of muscle synergies and the state-constrained Kalman filter. In 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 (pp. 802-805). [6696056] (International IEEE/EMBS Conference on Neural Engineering, NER). https://doi.org/10.1109/NER.2013.6696056