دورية أكاديمية

Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models.

التفاصيل البيبلوغرافية
العنوان: Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models.
المؤلفون: Anna Magdalena Vögele, Rebeka R Zsoldos, Björn Krüger, Theresia Licka
المصدر: PLoS ONE, Vol 11, Iss 6, p e0157239 (2016)
بيانات النشر: Public Library of Science (PLoS), 2016.
سنة النشر: 2016
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: This paper introduces a new method for data analysis of animal muscle activation during locomotion. It is based on fitting Gaussian mixture models (GMMs) to surface EMG data (sEMG). This approach enables researchers/users to isolate parts of the overall muscle activation within locomotion EMG data. Furthermore, it provides new opportunities for analysis and exploration of sEMG data by using the resulting Gaussian modes as atomic building blocks for a hierarchical clustering. In our experiments, composite peak models representing the general activation pattern per sensor location (one sensor on the long back muscle, three sensors on the gluteus muscle on each body side) were identified per individual for all 14 horses during walk and trot in the present study. Hereby we show the applicability of the method to identify composite peak models, which describe activation of different muscles throughout cycles of locomotion.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1932-6203
Relation: http://europepmc.org/articles/PMC4928879?pdf=render; https://doaj.org/toc/1932-6203
DOI: 10.1371/journal.pone.0157239
URL الوصول: https://doaj.org/article/c58590bba41e4b9c8400c93d7e87a5b0
رقم الأكسشن: edsdoj.58590bba41e4b9c8400c93d7e87a5b0
قاعدة البيانات: Directory of Open Access Journals