NDLI: A model for generating Surface EMG signal of m. Tibialis Anterior

Content Provider	IEEE Xplore Digital Library
Author	Siddiqi, A. Kumar, D. Arjunan, S.P.
Copyright Year	2014
Description	Author affiliation: Biosignals Lab., R. Melbourne Inst. of Technol. (RMIT) Univ., Melbourne, VIC, Australia (Siddiqi, A.; Kumar, D.; Arjunan, S.P.)
Abstract	A model that simulates surface electromyogram (sEMG) signal of m. Tibialis Anterior has been developed and tested. This has a firing rate equation that is based on experimental findings. It also has a recruitment threshold that is based on observed statistical distribution. Importantly, it has considered both, slow and fast type which has been distinguished based on their conduction velocity. This model has assumed that the deeper unipennate half of the muscle does not contribute significantly to the potential induced on the surface of the muscle and has approximated the muscle to have parallel structure. The model was validated by comparing the simulated and the experimental sEMG signal recordings. Experiments were conducted on eight subjects who performed isometric dorsiflexion at 10, 20, 30, 50, 75, and 100% maximal voluntary contraction. Normalized root mean square and median frequency of the experimental and simulated EMG signal were computed and the slopes of the linearity with the force were statistically analyzed. The gradients were found to be similar (p>0.05) for both experimental and simulated sEMG signal, validating the proposed model.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	106
Ending Page	109
File Size	643079
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Mathematical model Electromyography Firing Recruitment Computational modeling Electric potential
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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