Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time
Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes...
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| Published in | PloS one Vol. 16; no. 5; p. e0246611 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
26.05.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0246611 |
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| Abstract | Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm.
We collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task).
Support vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field.
By combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance. |
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| AbstractList | Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm. We collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task). Support vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field. By combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance. Background and objectiveDynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm.MethodsWe collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task).ResultsSupport vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field.ConclusionsBy combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance. Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm. We collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task). Support vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field. By combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance. Background and objective Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm. Methods We collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task). Results Support vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field. Conclusions By combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance. Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm.BACKGROUND AND OBJECTIVEDynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm.We collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task).METHODSWe collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task).Support vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field.RESULTSSupport vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field.By combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance.CONCLUSIONSBy combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance. |
| Audience | Academic |
| Author | Inan, Omer T. Rosa, Luis G. Zia, Jonathan S. Sawicki, Gregory S. |
| AuthorAffiliation | 2 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America 3 Emory University School of Medicine, Atlanta, Georgia, United States of America University of Illinois at Urbana-Champaign, UNITED STATES 1 School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America 4 School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America |
| AuthorAffiliation_xml | – name: 2 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America – name: 3 Emory University School of Medicine, Atlanta, Georgia, United States of America – name: 4 School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America – name: University of Illinois at Urbana-Champaign, UNITED STATES – name: 1 School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America |
| Author_xml | – sequence: 1 givenname: Luis G. orcidid: 0000-0002-9879-2223 surname: Rosa fullname: Rosa, Luis G. – sequence: 2 givenname: Jonathan S. surname: Zia fullname: Zia, Jonathan S. – sequence: 3 givenname: Omer T. surname: Inan fullname: Inan, Omer T. – sequence: 4 givenname: Gregory S. surname: Sawicki fullname: Sawicki, Gregory S. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34038426$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1249/00005768-199205000-00005 10.1152/japplphysiol.00802.2012 10.1038/s41598-020-60360-4 10.1152/japplphysiol.00253.2013 10.3389/fspor.2020.00069 10.1007/s00261-018-1517-0 10.1101/157479 10.1152/japplphysiol.00530.2011 10.1016/j.jneumeth.2007.09.022 10.1177/875647939000600106 10.1016/j.cmpb.2016.02.016 10.3390/jimaging4020029 10.1080/03091902.2020.1822940 10.1098/rspb.2019.2560 10.1016/j.medengphy.2008.11.004 10.1371/journal.pone.0241339 10.7717/peerj.7120 10.1152/japplphysiol.00835.2019 10.1073/pnas.1107972109 10.1080/10255842.2011.633516 10.1152/jappl.1993.74.2.520 10.1016/j.jbiomech.2018.03.013 10.1016/j.jacr.2019.06.004 10.7717/peerj.453 10.1016/j.cmpb.2020.105583 |
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| DOI | 10.1371/journal.pone.0246611 |
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| References | LJ Brattain (pone.0246611.ref004) 2018; 43 DJ Farris (pone.0246611.ref009) 2012; 109 JF Drazan (pone.0246611.ref014) 2019; 2019 E Rosten (pone.0246611.ref020) 2006 KR Müller (pone.0246611.ref019) 2008; 167 S Liu (pone.0246611.ref003) 2019 B Van Hooren (pone.0246611.ref006) 2020; 128 S Bohm (pone.0246611.ref007) 2019; 286 DJ Farris (pone.0246611.ref021) 2016; 128 ON Beck (pone.0246611.ref024) 2019; 47 F Pedregosa (pone.0246611.ref028) 2011 R Taylor (pone.0246611.ref030) 1990; 6 S Wang (pone.0246611.ref002) 2012 NJ Cronin (pone.0246611.ref015) 2020; 196 JG Gillett (pone.0246611.ref023) 2013; 16 DJ Farris (pone.0246611.ref010) 2012; 113 NJ Cronin (pone.0246611.ref022) 2011; 111 T Miyoshi (pone.0246611.ref012) 2009; 31 pone.0246611.ref018 Q Huang (pone.0246611.ref001) 2018 R Cunningham (pone.0246611.ref016) 2018; 4 ES Matijevich (pone.0246611.ref025) 2018; 72 JE Thorp (pone.0246611.ref037) 2020; 15 S Van Der Walt (pone.0246611.ref027) 2014; 2014 A Ebrahimi (pone.0246611.ref036) 2020; 2 J Fridén (pone.0246611.ref034) 1992; 24 pone.0246611.ref017 M-S Shang (pone.0246611.ref029) 2018; 324 Z Akkus (pone.0246611.ref005) 2019; 16 RL Lieber (pone.0246611.ref033) 1993; 74 RW Nuckols (pone.0246611.ref026) 2020; 10 pone.0246611.ref031 DJ Farris (pone.0246611.ref008) 2013; 115 GS Sawicki (pone.0246611.ref032) J Son (pone.0246611.ref011) 2020; 75 LK Kwah (pone.0246611.ref013) 2013 P Khera (pone.0246611.ref035) 2020; 44 |
| References_xml | – year: 2018 ident: pone.0246611.ref001 article-title: Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey publication-title: BioMed Research International – volume: 24 start-page: 521 year: 1992 ident: pone.0246611.ref034 article-title: Structural and mechanical basis of exercise-induced muscle injury publication-title: Med Sci Sports Exerc doi: 10.1249/00005768-199205000-00005 – volume: 324 year: 2018 ident: pone.0246611.ref029 article-title: Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model publication-title: IOP Conf Ser Mater Sci Eng – volume: 113 start-page: 1862 year: 2012 ident: pone.0246611.ref010 article-title: Linking the mechanics and energetics of hopping with elastic ankle exoskeletons publication-title: J Appl Physiol doi: 10.1152/japplphysiol.00802.2012 – volume: 10 start-page: 1 year: 2020 ident: pone.0246611.ref026 article-title: Ultrasound imaging links soleus muscle neuromechanics and energetics during human walking with elastic ankle exoskeletons publication-title: Sci Rep doi: 10.1038/s41598-020-60360-4 – ident: pone.0246611.ref018 – volume: 115 start-page: 579 year: 2013 ident: pone.0246611.ref008 article-title: Elastic ankle exoskeletons reduce soleus muscle force but not work in human hopping publication-title: J Appl Physiol doi: 10.1152/japplphysiol.00253.2013 – year: 2011 ident: pone.0246611.ref028 article-title: Scikit-learn: Machine Learning in Python publication-title: J Mach Learn Res – volume: 2 start-page: 69 year: 2020 ident: pone.0246611.ref036 article-title: Shear Wave Tensiometry Reveals an Age-Related Deficit in Triceps Surae Work at Slow and Fast Walking Speeds publication-title: Front Sport Act Living doi: 10.3389/fspor.2020.00069 – volume: 43 start-page: 786 year: 2018 ident: pone.0246611.ref004 article-title: Machine learning for medical ultrasound: status, methods, and future opportunities publication-title: Abdom Radiol doi: 10.1007/s00261-018-1517-0 – ident: pone.0246611.ref017 doi: 10.1101/157479 – volume: 111 start-page: 1491 year: 2011 ident: pone.0246611.ref022 article-title: Automatic tracking of medial gastrocnemius fascicle length during human locomotion publication-title: J Appl Physiol doi: 10.1152/japplphysiol.00530.2011 – start-page: 761 year: 2013 ident: pone.0246611.ref013 article-title: Reliability and validity of ultrasound measurements of muscle fascicle length and pennation in humans: A systematic review publication-title: Journal of Applied Physiology. American Physiological Society Bethesda, MD – start-page: 933 volume-title: Medical Image Analysis year: 2012 ident: pone.0246611.ref002 – volume: 167 start-page: 82 year: 2008 ident: pone.0246611.ref019 article-title: Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2007.09.022 – volume: 6 start-page: 35 year: 1990 ident: pone.0246611.ref030 article-title: Interpretation of the Correlation Coefficient: A Basic Review publication-title: J Diagnostic Med Sonogr doi: 10.1177/875647939000600106 – volume: 128 start-page: 111 year: 2016 ident: pone.0246611.ref021 article-title: UltraTrack: Software for semi-automated tracking of muscle fascicles in sequences of B-mode ultrasound images publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2016.02.016 – volume: 4 start-page: 29 year: 2018 ident: pone.0246611.ref016 article-title: Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks publication-title: J Imaging doi: 10.3390/jimaging4020029 – volume: 44 start-page: 441 year: 2020 ident: pone.0246611.ref035 article-title: Role of machine learning in gait analysis: a review publication-title: J Med Eng Technol doi: 10.1080/03091902.2020.1822940 – volume: 286 start-page: 20192560 year: 2019 ident: pone.0246611.ref007 article-title: The force–length–velocity potential of the human soleus muscle is related to the energetic cost of running publication-title: Proc R Soc B Biol Sci doi: 10.1098/rspb.2019.2560 – ident: pone.0246611.ref031 – volume: 31 start-page: 558 year: 2009 ident: pone.0246611.ref012 article-title: Automatic detection method of muscle fiber movement as revealed by ultrasound images publication-title: Med Eng Phys doi: 10.1016/j.medengphy.2008.11.004 – volume: 15 start-page: e0241339 year: 2020 ident: pone.0246611.ref037 article-title: Mechanisms of gait phase entrainment in healthy subjects during rhythmic electrical stimulation of the medial gastrocnemius publication-title: PLoS One doi: 10.1371/journal.pone.0241339 – volume: 2019 start-page: e7120 year: 2019 ident: pone.0246611.ref014 article-title: An automatic fascicle tracking algorithm quantifying gastrocnemius architecture during maximal effort contractions publication-title: PeerJ doi: 10.7717/peerj.7120 – volume: 75 year: 2020 ident: pone.0246611.ref011 article-title: Limited fascicle shortening and fascicle rotation may be associated with impaired voluntary force-generating capacity in pennate muscles of chronic stroke survivors publication-title: Clin Biomech – volume: 128 start-page: 978 year: 2020 ident: pone.0246611.ref006 article-title: Ultrasound imaging to assess skeletal muscle architecture during movements: a systematic review of methods, reliability, and challenges publication-title: J Appl Physiol doi: 10.1152/japplphysiol.00835.2019 – volume: 109 start-page: 977 year: 2012 ident: pone.0246611.ref009 article-title: Human medial gastrocnemius force-velocity behavior shifts with locomotion speed and gait publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.1107972109 – volume: 16 start-page: 678 year: 2013 ident: pone.0246611.ref023 article-title: Reliability and accuracy of an automated tracking algorithm to measure controlled passive and active muscle fascicle length changes from ultrasound publication-title: Comput Methods Biomech Biomed Engin doi: 10.1080/10255842.2011.633516 – volume: 47 year: 2019 ident: pone.0246611.ref024 article-title: Exoskeletons improve locomotion economy by reducing active muscle volume publication-title: Exerc Sport Sci Rev – volume: 74 start-page: 520 year: 1993 ident: pone.0246611.ref033 article-title: Muscle damage is not a function of muscle force but active muscle strain publication-title: J Appl Physiol doi: 10.1152/jappl.1993.74.2.520 – ident: pone.0246611.ref032 publication-title: The exoskeleton expansion: improving walking and running economy – volume: 72 start-page: 200 year: 2018 ident: pone.0246611.ref025 article-title: Ultrasound estimates of Achilles tendon exhibit unexpected shortening during ankle plantarflexion publication-title: J Biomech doi: 10.1016/j.jbiomech.2018.03.013 – volume: 16 start-page: 1318 year: 2019 ident: pone.0246611.ref005 article-title: A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow publication-title: J Am Coll Radiol doi: 10.1016/j.jacr.2019.06.004 – volume: 2014 start-page: e453 year: 2014 ident: pone.0246611.ref027 article-title: Scikit-image: Image processing in python publication-title: PeerJ doi: 10.7717/peerj.453 – start-page: 430 volume-title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) year: 2006 ident: pone.0246611.ref020 – volume: 196 start-page: 105583 year: 2020 ident: pone.0246611.ref015 article-title: Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2020.105583 – start-page: 261 volume-title: Engineering year: 2019 ident: pone.0246611.ref003 |
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| Snippet | Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding... Background and objective Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights... Background and objectiveDynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights... |
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| SubjectTerms | Adult Algorithms Ankle Ankle - diagnostic imaging Ankle - physiopathology Ankle Joint - diagnostic imaging Ankle Joint - physiopathology Automation Biology and Life Sciences Computer and Information Sciences Computer engineering Deep learning Drafting software Editing Evaluation Fasciculation - diagnostic imaging Fasciculation - physiopathology Female Funding Gait - physiology Humans Learning algorithms Locomotion - physiology Machine Learning Male Mechanical engineering Medicine and Health Sciences Methodology Muscle Contraction - physiology Muscle, Skeletal - diagnostic imaging Muscle, Skeletal - physiopathology Muscles Musculoskeletal system Physiology Real time Research and Analysis Methods Reviews Skeletal muscle Software Structure-function relationships Technology Training evaluation Ultrasonic imaging Ultrasonography Ultrasound Ultrasound imaging Walking - physiology Young Adult |
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| Title | Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time |
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