Decoding subtle forearm flexions using fractal features of surface Electromyogram from single and multiple sensors

Poosapadi Arjunan, S and Kumar, D 2010, 'Decoding subtle forearm flexions using fractal features of surface Electromyogram from single and multiple sensors', Journal of NeuroEngineering and Rehabilitation, vol. 7, no. 53, pp. 1-26.


Document type: Journal Article
Collection: Journal Articles

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Title Decoding subtle forearm flexions using fractal features of surface Electromyogram from single and multiple sensors
Author(s) Poosapadi Arjunan, S
Kumar, D
Year 2010
Journal name Journal of NeuroEngineering and Rehabilitation
Volume number 7
Issue number 53
Start page 1
End page 26
Total pages 26
Publisher BioMed Central Ltd
Abstract Identifying finger and wrist flexion based actions using a single channel surface electromyogram (sEMG) can lead to a number of applications such as sEMG based controllers for near elbow amputees, human computer interface (HCI) devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings.
Subject Signal Processing
Biomedical Engineering not elsewhere classified
DOI - identifier 10.1186/1743-0003-7-53
Copyright notice © 2010 Poosapadi Arjunan and Kumar , licensee BioMed Central Ltd.
ISSN 1743-0003
Additional Notes Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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