Extraction of muscle synergies using temporal segmentation of the record: A preliminary analysis

Peppino Tropea, Vito Monaco, Silvestro Micera

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

Abstract

Muscle synergies are considered as a potential strategy to reduce the computational workload undergoing the estimation of muscle activity during different motor tasks. They are usually extracted by means of algebraic factorization algorithms able to capture the greatest communality of a set of electromyographic (EMG) signals. Usually EMG signals are pooled across different sub-movements (e.g., going forward and backward during reaching) in order to increase the complexity of the data set and, consequently, capture the maximum communality. Despite of these, this preliminary study was designed to investigate how the communality of EMG signals can be explained looking at narrow subset of recorded signals. Results corroborate the hypothesis that using a suitable subset of the whole dataset can significantly modify the values of weight coefficients. In this regard, further methodological investigations of algorithms adopted for synergy extraction are still required.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages3624-3627
Number of pages4
DOIs
Publication statusPublished - 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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