Al-Mulla, MR and Sepulveda, F and Colley, MJ (2011) A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors, 2011 (11). pp. 3545-3594. DOI https://doi.org/10.3390/s110403545
Al-Mulla, MR and Sepulveda, F and Colley, MJ (2011) A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors, 2011 (11). pp. 3545-3594. DOI https://doi.org/10.3390/s110403545
Al-Mulla, MR and Sepulveda, F and Colley, MJ (2011) A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors, 2011 (11). pp. 3545-3594. DOI https://doi.org/10.3390/s110403545
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 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.
Item Type: | Article |
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Uncontrolled Keywords: | muscle fatigue; sEMG; feature extraction; classification |
Subjects: | R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 14 Aug 2012 11:10 |
Last Modified: | 30 Oct 2024 15:53 |
URI: | http://repository.essex.ac.uk/id/eprint/3448 |
Available files
Filename: sensors-11-03545-v2.pdf
Licence: Creative Commons: Attribution 3.0