Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Mohammed R. Al-Mulla"'
Publikováno v:
Sensors, Vol 14, Iss 6, Pp 9489-9504 (2014)
The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatig
Externí odkaz:
https://doaj.org/article/dba42bdadf034335ad71024c34187556
Publikováno v:
Proceedings of the 2018 International Conference on Information Science and System.
SEMG electrodes are applied in numerous studies on muscle fatigue, utilised to detect the fatigue in various muscle groups analysed by a range of feature extraction methods. Newer research are applying multi-channel electrodes with the aim of increas
Publikováno v:
International Journal of Biomedical Engineering and Technology. 30:277
This study compares the classification performance of both sEMG and MMG signal from fatiguing dynamic contraction of the biceps brachii. Commonly used statistical features are compared with a recently developed evolved pseudo-wavelet. Based on the li
Publikováno v:
Medical Engineering & Physics. 33:411-417
The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps muscle was recorded from ten subjects performing semi-isometric (i.e., attempted i
Publikováno v:
Sensors; Volume 11; Issue 2; Pages: 1542-1557
Sensors, Vol 11, Iss 2, Pp 1542-1557 (2011)
Sensors (Basel, Switzerland)
Sensors, Vol 11, Iss 2, Pp 1542-1557 (2011)
Sensors (Basel, Switzerland)
Muscle fatigue is an established area of research and various types of muscle fatigue have been clinically investigated in order to fully understand the condition. This paper demonstrates a non-invasive technique used to automate the fatigue detectio
Publikováno v:
Sensors, Vol 10, Iss 5, Pp 4838-4854 (2010)
Sensors (Basel, Switzerland)
Sensors; Volume 10; Issue 5; Pages: 4838-4854
Sensors (Basel, Switzerland)
Sensors; Volume 10; Issue 5; Pages: 4838-4854
Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects performing isometric contraction until fatigue. A novel feature (1D spectro_std) was used to extract the feature that modeled three classes of fatigue, which
Publikováno v:
Computers
Volume 4
Issue 3
Pages 251-264
Computers, Vol 4, Iss 3, Pp 251-264 (2015)
Volume 4
Issue 3
Pages 251-264
Computers, Vol 4, Iss 3, Pp 251-264 (2015)
Surface electromyographic (sEMG) activity of the biceps muscle was recorded from 13 subjects. Data was recorded while subjects performed dynamic contraction until fatigue and the signals were segmented into two parts (Non-Fatigue and Fatigue). An evo
Publikováno v:
Bioinformatics and Biomedical Engineering ISBN: 9783319164823
IWBBIO (1)
IWBBIO (1)
Mechanomyography (MMG) activity of the biceps muscle was recorded from thirteen subjects. Data was recorded while subjects performed dynamic contraction until fatigue. The signals were segmented into two parts (Non-Fatigue and Fatigue), An evolutiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f0d02e9b1114b1a3b66f1b3eb40377f
https://doi.org/10.1007/978-3-319-16483-0_31
https://doi.org/10.1007/978-3-319-16483-0_31
Publikováno v:
Journal of medical systems. 39(1)
In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-wavelet function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surfac
Autor:
Mohammed R. Al-Mulla
Publikováno v:
2012 4th Computer Science and Electronic Engineering Conference (CEEC).
This study developed a new muscle fatigue feature based on sEMG signals. The evolved feature is combining 11 traditional muscle fatigue sEMG parameters to optimally classify the sEMG signals. The myoelectric signals were recorded from 13 subjects per