A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments
Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xu, Xu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Emerging roles of miRNAs in the development of pancreatic cancer - Fathi, Mohadeseh ELSEVIER, 2021, official journal of the International Society of Electrophysiology and Kinesiology, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:24 ; year:2014 ; number:3 ; pages:419-429 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.jelekin.2014.02.004 |
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ELV034135391 |
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| 245 | 1 | 0 | |a A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments |
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| 520 | |a Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. | ||
| 520 | |a Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. | ||
| 650 | 7 | |a Biomechanical model |2 Elsevier | |
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| 700 | 1 | |a McGorry, Raymond W. |4 oth | |
| 700 | 1 | |a Lin, Jia-Hua |4 oth | |
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10.1016/j.jelekin.2014.02.004 doi GBVA2014017000028.pica (DE-627)ELV034135391 (ELSEVIER)S1050-6411(14)00036-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.40 bkl Xu, Xu verfasserin aut A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Biomechanical model Elsevier Kinematics Elsevier Muscle co-contraction Elsevier EMG Elsevier McGorry, Raymond W. oth Lin, Jia-Hua oth Enthalten in Elsevier Science Fathi, Mohadeseh ELSEVIER Emerging roles of miRNAs in the development of pancreatic cancer 2021 official journal of the International Society of Electrophysiology and Kinesiology Amsterdam [u.a.] (DE-627)ELV006547923 volume:24 year:2014 number:3 pages:419-429 extent:11 https://doi.org/10.1016/j.jelekin.2014.02.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 24 2014 3 419-429 11 045F 610 |
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10.1016/j.jelekin.2014.02.004 doi GBVA2014017000028.pica (DE-627)ELV034135391 (ELSEVIER)S1050-6411(14)00036-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.40 bkl Xu, Xu verfasserin aut A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Biomechanical model Elsevier Kinematics Elsevier Muscle co-contraction Elsevier EMG Elsevier McGorry, Raymond W. oth Lin, Jia-Hua oth Enthalten in Elsevier Science Fathi, Mohadeseh ELSEVIER Emerging roles of miRNAs in the development of pancreatic cancer 2021 official journal of the International Society of Electrophysiology and Kinesiology Amsterdam [u.a.] (DE-627)ELV006547923 volume:24 year:2014 number:3 pages:419-429 extent:11 https://doi.org/10.1016/j.jelekin.2014.02.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 24 2014 3 419-429 11 045F 610 |
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10.1016/j.jelekin.2014.02.004 doi GBVA2014017000028.pica (DE-627)ELV034135391 (ELSEVIER)S1050-6411(14)00036-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.40 bkl Xu, Xu verfasserin aut A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Biomechanical model Elsevier Kinematics Elsevier Muscle co-contraction Elsevier EMG Elsevier McGorry, Raymond W. oth Lin, Jia-Hua oth Enthalten in Elsevier Science Fathi, Mohadeseh ELSEVIER Emerging roles of miRNAs in the development of pancreatic cancer 2021 official journal of the International Society of Electrophysiology and Kinesiology Amsterdam [u.a.] (DE-627)ELV006547923 volume:24 year:2014 number:3 pages:419-429 extent:11 https://doi.org/10.1016/j.jelekin.2014.02.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 24 2014 3 419-429 11 045F 610 |
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10.1016/j.jelekin.2014.02.004 doi GBVA2014017000028.pica (DE-627)ELV034135391 (ELSEVIER)S1050-6411(14)00036-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.40 bkl Xu, Xu verfasserin aut A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Biomechanical model Elsevier Kinematics Elsevier Muscle co-contraction Elsevier EMG Elsevier McGorry, Raymond W. oth Lin, Jia-Hua oth Enthalten in Elsevier Science Fathi, Mohadeseh ELSEVIER Emerging roles of miRNAs in the development of pancreatic cancer 2021 official journal of the International Society of Electrophysiology and Kinesiology Amsterdam [u.a.] (DE-627)ELV006547923 volume:24 year:2014 number:3 pages:419-429 extent:11 https://doi.org/10.1016/j.jelekin.2014.02.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 24 2014 3 419-429 11 045F 610 |
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10.1016/j.jelekin.2014.02.004 doi GBVA2014017000028.pica (DE-627)ELV034135391 (ELSEVIER)S1050-6411(14)00036-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.40 bkl Xu, Xu verfasserin aut A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. Biomechanical model Elsevier Kinematics Elsevier Muscle co-contraction Elsevier EMG Elsevier McGorry, Raymond W. oth Lin, Jia-Hua oth Enthalten in Elsevier Science Fathi, Mohadeseh ELSEVIER Emerging roles of miRNAs in the development of pancreatic cancer 2021 official journal of the International Society of Electrophysiology and Kinesiology Amsterdam [u.a.] (DE-627)ELV006547923 volume:24 year:2014 number:3 pages:419-429 extent:11 https://doi.org/10.1016/j.jelekin.2014.02.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 24 2014 3 419-429 11 045F 610 |
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Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. 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a regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments |
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A regression model predicting isometric shoulder muscle activities from arm postures and shoulder joint moments |
| abstract |
Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. |
| abstractGer |
Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. |
| abstract_unstemmed |
Tissue overloading is a major contributor to shoulder musculoskeletal injuries. Previous studies attempted to use regression-based methods to predict muscle activities from shoulder kinematics and shoulder kinetics. While a regression-based method can address co-contraction of the antagonist muscles as opposed to the optimization method, most of these regression models were based on limited shoulder postures. The purpose of this study was to develop a set of regression equations to predict the 10th percentile, the median, and the 90th percentile of normalized electromyography (nEMG) activities from shoulder postures and net shoulder moments. Forty participants generated various 3-D shoulder moments at 96 static postures. The nEMG of 16 shoulder muscles was measured and the 3-D net shoulder moment was calculated using a static biomechanical model. A stepwise regression was used to derive the regression equations. The results indicated the measured range of the 3-D shoulder moment in this study was similar to those observed during work requiring light physical capacity. The r 2 of all the regression equations ranged between 0.228 and 0.818. For the median of the nEMG, the average r 2 among all 16 muscles was 0.645, and the five muscles with the greatest r 2 were the three deltoids, supraspinatus, and infraspinatus. The results can be used by practitioners to estimate the range of the shoulder muscle activities given a specific arm posture and net shoulder moment. |
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