1
|
Toro SFD, Santos-Cuadros S, Olmeda E, Álvarez-Caldas C, Díaz V, San Román JL. Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue? SENSORS 2019; 19:s19143204. [PMID: 31330807 PMCID: PMC6679263 DOI: 10.3390/s19143204] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/08/2019] [Accepted: 07/18/2019] [Indexed: 01/21/2023]
Abstract
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.
Collapse
Affiliation(s)
- Sergio Fuentes Del Toro
- Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain.
- Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain.
| | - Silvia Santos-Cuadros
- Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
- Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
| | - Ester Olmeda
- Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
- Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
| | - Carolina Álvarez-Caldas
- Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
- Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
| | - Vicente Díaz
- Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
- Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
| | - José Luís San Román
- Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
- Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
| |
Collapse
|
2
|
Hazarika A, Barthakur M, Dutta L, Bhuyan M. F-SVD based algorithm for variability and stability measurement of bio-signals, feature extraction and fusion for pattern recognition. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
3
|
Venkatakrishnan P, Sangeetha S. Singularity detection in human EEG signal using wavelet leaders. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
4
|
Doulah ABMSU, Fattah SA, Zhu WP, Ahmad MO. DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification. Healthc Technol Lett 2014; 1:26-31. [PMID: 26609372 DOI: 10.1049/htl.2013.0036] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Revised: 03/14/2014] [Accepted: 03/17/2014] [Indexed: 11/20/2022] Open
Abstract
A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching-based decomposition technique. Unlike conventional MUAP-based methods, only one MUAP with maximum dynamic range is selected for DCT-based feature extraction. Magnitude and frequency values of a few high-energy DCT coefficients corresponding to the selected MUAP are used as the desired feature which not only reduces computational burden, but also offers better feature quality with high within-class compactness and between-class separation. For the purpose of classification, the K-nearest neighbourhood classifier is employed. Extensive analysis is performed on clinical EMG database and it is found that the proposed method provides a very satisfactory performance in terms of specificity, sensitivity and overall classification accuracy.
Collapse
Affiliation(s)
| | | | - Wei-Ping Zhu
- Department of Electrical and Computer Engineering , Concordia University , Montreal , QC , H3G 1M8 , Canada
| | - M Omair Ahmad
- Department of Electrical and Computer Engineering , Concordia University , Montreal , QC , H3G 1M8 , Canada
| |
Collapse
|
5
|
Doulah ABMSU, Fattah SA, Zhu WP, Ahmad MO. Wavelet domain feature extraction scheme based on dominant motor unit action potential of EMG signal for neuromuscular disease classification. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:155-164. [PMID: 24759993 DOI: 10.1109/tbcas.2014.2309252] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, two schemes for neuromuscular disease classification from electromyography (EMG) signals are proposed based on discrete wavelet transform (DWT) features. In the first scheme, a few high energy DWT coefficients along with the maximum value are extracted in a frame by frame manner from the given EMG data. Instead of considering only such local information obtained from a single frame, we propose to utilize global statistics which is obtained based on information collected from some consecutive frames. In the second scheme, motor unit action potentials (MUAPs) are first extracted from the EMG data via template matching based decomposition technique. It is well known that not all MUAPs obtained via decomposition are capable of uniquely representing a class. Thus, a novel idea of selecting a dominant MUAP, based on energy criterion, is proposed and instead of all MUAPs, only the dominant MUAP is used for the classification. A feature extraction scheme based on some statistical properties of the DWT coefficients of dominant MUAPs is proposed. For the purpose of classification, the K-nearest neighborhood (KNN) classifier is employed. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms of specificity, sensitivity, and overall classification accuracy.
Collapse
|
6
|
Towards identification of finger flexions using single channel surface electromyography--able bodied and amputee subjects. J Neuroeng Rehabil 2013; 10:50. [PMID: 23758881 PMCID: PMC3680228 DOI: 10.1186/1743-0003-10-50] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2012] [Accepted: 05/25/2013] [Indexed: 11/10/2022] Open
Abstract
Background This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity. Methods SEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location. Results Accuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation. Conclusions Volume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification.
Collapse
|
8
|
Poosapadi Arjunan S, Kumar DK. Computation of fractal features based on the fractal analysis of surface Electromyogram to estimate force of contraction of different muscles. Comput Methods Biomech Biomed Engin 2012; 17:210-6. [DOI: 10.1080/10255842.2012.675055] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
9
|
Talebinejad M, Chan ADC, Miri A. Multiplicative multi-fractal modeling of electromyography signals for discerning neuropathic conditions. J Electromyogr Kinesiol 2010; 20:1244-8. [PMID: 20705483 DOI: 10.1016/j.jelekin.2010.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2009] [Revised: 07/15/2010] [Accepted: 07/15/2010] [Indexed: 10/19/2022] Open
Abstract
In this paper, we present a new method for multi-scale analysis of electromyography signals based on an interesting fractal process known as multiplicative cascade multi-fractal. Using simulated needle electromyography signals, we show this method provides a means for discrimination of normal and neuropathic electromyography signals. We also present experimental results that show the new parameters, computed using multiplicative cascade multi-fractal modeling, are more robust than the conventional signal parameter, number of turns, in the presence of additive noise. Results of multiplicative cascade multi-fractal modeling are consistent with other multi-scale approaches; advantages and differences are high lighted.
Collapse
|
10
|
Patil S, Abel EW. Real time continuous wavelet transform implementation on a DSP processor. J Med Eng Technol 2009; 33:223-31. [DOI: 10.1080/03091900802697867] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|