NDLI: Fractal feature of sEMG from Flexor digitorum superficialis muscle correlated with levels of contraction during low-level finger flexions

Content Provider	IEEE Xplore Digital Library
Author	Arjunan, S.P. Kumar, D.K. Naik, G.R.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Computer Engineering, RMIT university, Melbourne, VIC 3001, Australia (Arjunan, S.P.; Kumar, D.K.; Naik, G.R.)
Abstract	This research paper reports an experimental study on identification of the changes in fractal properties of surface Electromyogram (sEMG) with the changes in the force levels during low-level finger flexions. In the previous study, the authors have identified a novel fractal feature, Maximum fractal length (MFL) as a measure of strength of low-level contractions and has used this feature to identify various wrist and finger movements. This study has tested the relationship between the MFL and force of contraction. The results suggest that changes in MFL is correlated with the changes in contraction levels (20%, 50% and 80% maximum voluntary contraction (MVC)) during low-level muscle activation such as finger flexions. From the statistical analysis and by visualisation using box-plot, it is observed that MFL (p ≈ 0.001) is a more correlated to force of contraction compared to RMS (p≈0.05), even when the muscle contraction is less than 50% MVC during low-level finger flexions. This work has established that this fractal feature will be useful in providing information about changes in levels of force during low-level finger movements for prosthetic control or human computer interface.
Starting Page	4614
Ending Page	4617
File Size	734307
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626468
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fractals Muscles Force Electromyography Force measurement Conferences Skin
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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