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Search Results (4)

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Keywords = localised muscle fatigue

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14 pages, 284 KiB  
Article
Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction
by Mohamed R. Al-Mulla, Francisco Sepulveda and Bader Al-Bader
Computers 2015, 4(3), 251-264; https://doi.org/10.3390/computers4030251 - 10 Sep 2015
Cited by 4 | Viewed by 6871
Abstract
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 evolutionary algorithm was used to determine the elbow [...] Read more.
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 evolutionary algorithm was used to determine the elbow angles that best separate (using Davies-Bouldin Index, DBI) both Non-Fatigue and Fatigue segments of the sEMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted sEMG trials. After completing 26 independent evolution runs, the best run containing the optimal elbow angles for separation (Non-Fatigue and Fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on nine features extracted from each of the two classes (Non-Fatigue and Fatigue) to quantify the performance. Results showed that the optimal elbow angles can be used for fatigue classification, showing 87.90% highest correct classification for one of the features and on average of all eight features (including worst performing features) giving 78.45%. Full article
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16 pages, 3230 KiB  
Article
Novel Pseudo-Wavelet Function for MMG Signal Extraction during Dynamic Fatiguing Contractions
by Mohammed R. Al-Mulla and Francisco Sepulveda
Sensors 2014, 14(6), 9489-9504; https://doi.org/10.3390/s140609489 - 28 May 2014
Cited by 18 | Viewed by 7583
Abstract
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 fatigue. For training and testing purposes, the signals [...] Read more.
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 fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0:05). Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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50 pages, 2007 KiB  
Review
A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue
by Mohamed R. Al-Mulla, Francisco Sepulveda and Martin Colley
Sensors 2011, 11(4), 3545-3594; https://doi.org/10.3390/s110403545 - 24 Mar 2011
Cited by 247 | Viewed by 26607
Abstract
Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as [...] Read more.
Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results. Full article
(This article belongs to the Special Issue Sensors in Biomechanics and Biomedicine)
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16 pages, 2878 KiB  
Article
An Autonomous Wearable System for Predicting and Detecting Localised Muscle Fatigue
by Mohamed R. Al-Mulla, Francisco Sepulveda and Martin Colley
Sensors 2011, 11(2), 1542-1557; https://doi.org/10.3390/s110201542 - 27 Jan 2011
Cited by 38 | Viewed by 12153
Abstract
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 detection and prediction process. The system utilises [...] Read more.
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 detection and prediction process. The system utilises the clinical aspects such as kinematics and surface electromyography (sEMG) of an athlete during isometric contractions. Various signal analysis methods are used illustrating their applicability in real-time settings. This demonstrated system can be used in sports scenarios to promote muscle growth/performance or prevent injury. To date, research on localised muscle fatigue focuses on the clinical side and lacks the implementation for detecting/predicting localised muscle fatigue using an autonomous system. Results show that automating the process of localised muscle fatigue detection/prediction is promising. The autonomous fatigue system was tested on five individuals showing 90.37% accuracy on average of correct classification and an error of 4.35% in predicting the time to when fatigue will onset. Full article
(This article belongs to the Section Physical Sensors)
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