Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions
<italic>Goal:</italic> This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10646524/ |
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| author | Giovanni Corvini Michail Arvanitidis Deborah Falla Silvia Conforto |
| author_facet | Giovanni Corvini Michail Arvanitidis Deborah Falla Silvia Conforto |
| author_sort | Giovanni Corvini |
| collection | DOAJ |
| description | <italic>Goal:</italic> This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. <italic>Methods:</italic> Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. <italic>Results:</italic> Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. <italic>Conclusions:</italic> These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure. |
| format | Article |
| id | doaj-art-7aaac72450f14b139fc413e59b607a30 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-7aaac72450f14b139fc413e59b607a302025-01-30T00:03:47ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01576076810.1109/OJEMB.2024.344954810646524Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric ContractionsGiovanni Corvini0https://orcid.org/0000-0001-9057-577XMichail Arvanitidis1https://orcid.org/0000-0002-3339-6668Deborah Falla2https://orcid.org/0000-0003-1689-6190Silvia Conforto3https://orcid.org/0000-0001-7323-5220Department of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Rome, ItalySchool of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, U.K.School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, U.K.Department of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Rome, Italy<italic>Goal:</italic> This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. <italic>Methods:</italic> Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. <italic>Results:</italic> Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. <italic>Conclusions:</italic> These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.https://ieeexplore.ieee.org/document/10646524/HD-sEMGmuscle fatiguespatial muscle distributionspatiotemporal analysisendurance time |
| spellingShingle | Giovanni Corvini Michail Arvanitidis Deborah Falla Silvia Conforto Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions IEEE Open Journal of Engineering in Medicine and Biology HD-sEMG muscle fatigue spatial muscle distribution spatiotemporal analysis endurance time |
| title | Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions |
| title_full | Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions |
| title_fullStr | Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions |
| title_full_unstemmed | Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions |
| title_short | Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions |
| title_sort | novel metrics for high density semg analysis in the time x2013 space domain during sustained isometric contractions |
| topic | HD-sEMG muscle fatigue spatial muscle distribution spatiotemporal analysis endurance time |
| url | https://ieeexplore.ieee.org/document/10646524/ |
| work_keys_str_mv | AT giovannicorvini novelmetricsforhighdensitysemganalysisinthetimex2013spacedomainduringsustainedisometriccontractions AT michailarvanitidis novelmetricsforhighdensitysemganalysisinthetimex2013spacedomainduringsustainedisometriccontractions AT deborahfalla novelmetricsforhighdensitysemganalysisinthetimex2013spacedomainduringsustainedisometriccontractions AT silviaconforto novelmetricsforhighdensitysemganalysisinthetimex2013spacedomainduringsustainedisometriccontractions |