Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25033
Title: Design Validation of a Low-Cost EMG Sensor Compared to a Commercial-Based System for Measuring Muscle Activity and Fatigue
Authors: Bawa, A
Banitsas, K
Keywords: electromyography;low-cost sensor;muscle contraction;fatigue assessment
Issue Date: 3-Aug-2022
Publisher: MDPI
Citation: Bawa, A. and Banitsas, K. (2022) 'Design Validation of a Low-Cost EMG Sensor Compared to a Commercial-Based System for Measuring Muscle Activity and Fatigue', Sensors, 22, 5799, pp. 1 - 15. doi: 10.3390/s22155799.
Abstract: Copyright: © 2022 by the authors. Electromyography (EMG) sensors have been used for measuring muscle signals and for diagnosing neuromuscular disease. Available commercial EMG sensor are expensive and not easily available for individuals. The aim of the study is to validate our designed low-cost sensor against a well-known commercial system for measuring muscle activity and fatigue assessment. The evaluation of the designed system was done through a series of dynamic exercises performed by volunteers. Our low-cost EMG sensor and the commercially available system were placed on the vastus lateralis muscle to concurrently record the signal in a maximum voluntary contraction (MVC). The signal analysis was done using two validation indicators: Spearman’s correlation, and intra-class cross correlation on SPSS 26.0 version. For the muscle fatigue assessment, the root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) indicators were used. The results at the peak and mean level muscle contraction intensity were computed. The relative agreement for the two systems was excellent at peak level muscle contraction range (ICC 0.74–0.92), average 0.83 and mean level muscle contraction intensity range (ICC 0.65–0.85) with an average of 0.74. The Spearman’s correlation average was 0.76 with the range of (0.71–0.85) at peak level contraction, whiles the mean level contraction average was 0.71 at a range of (0.62–0.81). In determining muscle fatigue, the RMS and MAV showed increasing values in the time domain, while the MEF decreased in the frequency domain. Overall, the results indicated a good to excellent agreement of the two systems and confirmed the reliability of our design. The low-cost sensor also proved to be suitable for muscle fatigue assessment. Our designed system can therefore be implemented for rehabilitation, sports science, and ergonomics.
Description: Data Availability Statement: Data available on request to corresponding author due to ethical restrictions.
URI: https://bura.brunel.ac.uk/handle/2438/25033
DOI: https://doi.org/10.3390/s22155799
Other Identifiers: 5799
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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