Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles
Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activit...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1024 |
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| author | Kaci E. Madden Dragan Djurdjanovic Ashish D. Deshpande |
| author_facet | Kaci E. Madden Dragan Djurdjanovic Ashish D. Deshpande |
| author_sort | Kaci E. Madden |
| collection | DOAJ |
| description | Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub><mo>=</mo><mo>−</mo><mn>0.86</mn></mrow></semantics></math></inline-formula>) and ratings of perceived exertion (RPE) (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub><mo>=</mo><mn>0.87</mn></mrow></semantics></math></inline-formula>), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue. |
| first_indexed | 2024-03-09T05:58:57Z |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:58:57Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-23ba2893faf345a3b4bcd1e42b7ed4342023-12-03T12:11:06ZengMDPI AGSensors1424-82202021-02-01214102410.3390/s21041024Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor MusclesKaci E. Madden0Dragan Djurdjanovic1Ashish D. Deshpande2Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USADepartment of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USADepartment of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USACurrent methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub><mo>=</mo><mo>−</mo><mn>0.86</mn></mrow></semantics></math></inline-formula>) and ratings of perceived exertion (RPE) (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>r</mi><mrow><mi>r</mi><mi>m</mi></mrow></msub><mo>=</mo><mn>0.87</mn></mrow></semantics></math></inline-formula>), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.https://www.mdpi.com/1424-8220/21/4/1024human fatigue monitoringneuromuscular fatiguesurface electromyography time-frequency signal analysistime-series modelingautoregressive moving average model with exogenous inputsisometric contraction |
| spellingShingle | Kaci E. Madden Dragan Djurdjanovic Ashish D. Deshpande Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles Sensors human fatigue monitoring neuromuscular fatigue surface electromyography time-frequency signal analysis time-series modeling autoregressive moving average model with exogenous inputs isometric contraction |
| title | Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles |
| title_full | Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles |
| title_fullStr | Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles |
| title_full_unstemmed | Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles |
| title_short | Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles |
| title_sort | using a system based monitoring paradigm to assess fatigue during submaximal static exercise of the elbow extensor muscles |
| topic | human fatigue monitoring neuromuscular fatigue surface electromyography time-frequency signal analysis time-series modeling autoregressive moving average model with exogenous inputs isometric contraction |
| url | https://www.mdpi.com/1424-8220/21/4/1024 |
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