Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas

En el presente documento se presenta el desarrollo de un modelo de predicción de fatiga muscular en el antebrazo, basado en señales de electromiografía superficial, para la adquisición de estas señales será utilizado el dispositivo Myo el cual cuenta con 8 electrodos integrados y un protocolo de com...

Full description

Autores:
Ramirez Hernandez, Juan David
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad Militar Nueva Granada
Repositorio:
Repositorio UMNG
Idioma:
spa
OAI Identifier:
oai:repository.umng.edu.co:10654/47625
Acceso en línea:
https://hdl.handle.net/10654/47625
Palabra clave:
Fatiga
Epicondilitis
Señales
Biomecanica
Entropia
Espacio de estados
SVM
Electromiografia
Muscle Fatigue
Epicondylitis
Signals
Biomechanics
Entropy
State Space
SVM (Support Vector Machine)
Electromyography
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
dc.title.eng.fl_str_mv Development of a muscle fatigue model using EMG for young tennis players.
title Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
spellingShingle Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
Fatiga
Epicondilitis
Señales
Biomecanica
Entropia
Espacio de estados
SVM
Electromiografia
Muscle Fatigue
Epicondylitis
Signals
Biomechanics
Entropy
State Space
SVM (Support Vector Machine)
Electromyography
title_short Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
title_full Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
title_fullStr Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
title_full_unstemmed Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
title_sort Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistas
dc.creator.fl_str_mv Ramirez Hernandez, Juan David
dc.contributor.advisor.none.fl_str_mv Rubiano Fonseca, Astrid
dc.contributor.author.none.fl_str_mv Ramirez Hernandez, Juan David
dc.contributor.other.none.fl_str_mv Ramirez, Jose Luis
dc.subject.proposal.spa.fl_str_mv Fatiga
Epicondilitis
Señales
Biomecanica
Entropia
Espacio de estados
SVM
Electromiografia
topic Fatiga
Epicondilitis
Señales
Biomecanica
Entropia
Espacio de estados
SVM
Electromiografia
Muscle Fatigue
Epicondylitis
Signals
Biomechanics
Entropy
State Space
SVM (Support Vector Machine)
Electromyography
dc.subject.proposal.eng.fl_str_mv Muscle Fatigue
Epicondylitis
Signals
Biomechanics
Entropy
State Space
SVM (Support Vector Machine)
Electromyography
description En el presente documento se presenta el desarrollo de un modelo de predicción de fatiga muscular en el antebrazo, basado en señales de electromiografía superficial, para la adquisición de estas señales será utilizado el dispositivo Myo el cual cuenta con 8 electrodos integrados y un protocolo de comunicación Bluetooth. Para esto se propone un análisis de las características como Entropía (H), Derivada de la Entropía ( ̇H), valor eficaz (RMS), Valor absoluto medio (MAV) y Varianza (VAR). Se determina la linealidad del sistema por la distancia euclidiana entre los espacios de características, posteriormente se alimenta un sistema SVM lineal para la segmentación de los grupos de datos. Finalmente, los resultados obtenidos permitieron el desarrollo de un modelo predictivo de fatiga muscular con una predicción asertiva del 79.3 % con una validación cruzada, y se obtuvieron resultados de 87.5 % y 91.7 % de predicción asertiva en las pruebas del modelo entrenado.
publishDate 2024
dc.date.issued.none.fl_str_mv 2024-04-13
dc.date.accessioned.none.fl_str_mv 2025-11-06T16:50:46Z
dc.date.available.none.fl_str_mv 2025-11-06T16:50:46Z
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dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Militar Nueva Granada
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identifier_str_mv instname:Universidad Militar Nueva Granada
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dc.language.iso.none.fl_str_mv spa
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dc.relation.references.spa.fl_str_mv balandinodidonato. (2016). Myotoolkit/software for thalmic’s myo armband.md at master · balandinodidonato/myotoolkit · github. Descargado de https://github.com/balandinodidonato/MyoToolkit/blob/master/Software%20for%20Thalmic%27s%20Myo%20armband.md
Boon-Leng, L., Dae-Seok, L., y Boon-Giin, L. (2016, 1). Mobile-based wearable-type of driver fatigue detection by gsr and emg. IEEE Region 10 Annual International Conference, Proceedings/TENCON , 2016-January. doi: 10.1109/TENCON.2015.7372932
Castiblanco, P. A., Ramirez, J. L., y Rubiano, A. (2021). Smart materials and their application in robotic hand systems: A state of the art. Indonesian Journal of Science and Technology, 6 , 401-426. Descargado de https://ejournal.upi.edu/index.php/ijost/article/view/35630 doi: 10.17509/IJOST.V6I2.35630
Cutts, S., Gangoo, S., Modi, N., y Pasapula, C. (2019, 1). Tennis elbow: A clinical review article. Journal of orthopaedics, 17 , 203-207. Descargado de https://pubmed.ncbi.nlm.nih.gov/31889742/ doi: 10.1016/J.JOR.2019.08.005
Furui, A., y Tsuji, T. (2019, 7). Muscle fatigue analysis by using a scale mixture-based stochastic model of surface emg signals. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , 1948-1951. doi: 10.1109/EMBC.2019.8856348
Girard, O., y egoire Millet, G. P. (2009). Neuromuscular fatigue in racquet spor ts. PhysMed Rehabil Clin N Am, 26 , 161-173. doi: 10.1016/j.pmr.2008.10.008
Jero, S. E., y Ramakrishnan, S. (2019, 7). Analysis of muscle fatigue conditions in surface emg signal with a novel hilbert marginal spectrum entropy method. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , 2675-2678. doi: 10.1109/EMBC.2019.8857077
Jinpyeo Jeung, Y. C. I. Y. (2020). Quantitative muscle fatigue estimation with high snr flexible skin electrode. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). doi: 10.0/Linux-x86_64
Khanam, F., y Ahmad, M. (2016, 3). Frequency based emg power spectrum analysis of salat associated muscle contraction. ICEEE 2015 - 1st International Conference on Electrical and Electronic Engineering, 161-164. doi: 10.1109/CEEE.2015.7428245
Labs, T. (2020). Thalmic labs · github. Descargado de https://github.com/thalmiclabs
Larson, D. J., y Brown, S. H. (2022, 3). Influence of back muscle fatigue on dynamic lumbar spine stability and coordination variability of the thorax-pelvis during repetitive flexion-extension movements. Journal of biomechanics, 133 . Descargado de https://pubmed.ncbi.nlm.nih.gov/35081464/ doi: 10.1016/J.JBIOMECH.2022.110959
McDonald, A. C., Mulla, D. M., y Keir, P. J. (2019, 2). Muscular and kinematic adaptations to fatiguing repetitive upper extremity work. Applied Ergonomics, 75 , 250-256. doi: 10.1016/J.APERGO.2018.11.001
Na, Y., Lee, H.-D., y Kim, J. (2015). Muscle fatigue estimation with twitch force derived from semg peaks. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi: 10.1109/EMBC.2015.7319145
Nathalli G. Readi, A. B. T. M. V., Lorenzo Schwarcke. (2015). The effect of lymph drainage on the myoelectric manifestation of vastus lateralis fatigue: Preliminary results. doi: 10.1109/EMBC.2015.7319923
Ping, N., y Yang, J. (2022, 5). Exercise fatigue injury under sport resistance. Revista Brasileira de Medicina do Esporte, 28 , 682-685. Descargado de https://www.scielo.br/j/rbme/a/8sK3DjCHRkTMDYxVfZWTZXQ/ doi: 10.1590/1517-8692202228062022_0088
Qi, M. S., Yang, W. J., Xie, P., Liu, Z. J., Zhang, Y. Y., y Cheng, S. C. (2019, 1). Driver fatigue assessment based on the feature fusion and transfer learning of eeg and emg. Proceedings 2018 Chinese Automation Congress, CAC 2018 , 1314-1317. doi: 10.1109/CAC.2018.8623087
Rubiano, A., Ramirez, J. L., y Moreno, R. J. (2019). Comparison of features for semg based detection of hand movement inception using a wearable device. , 14 .
Rubiano, A., Ramirez, J. L., Rubiano, A., y Ramirez, J. L. (2019, 3). Grasping recognition based on electromyographic signals using a wearable device. International Review of Automatic Control (IREACO), 12 , 95-101. Descargado de https://www.praiseworthyprize.org/jsm/ index.php?journal=ireaco&page=article&op=view&path[]=23076 doi: 10.15866/IREACO.V12I2.16267
Sasidharan, D., y Venugopal, G. (2020, 7). Analysis of surface emg signals under fatigue conditions using sum of sines models. Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020 , 849-852. doi: 10.1109/ICCSP48568.2020.9182049
Zhang, G., Morin, E., Zhang, Y., y Etemad, S. A. (2018, 7). Non-invasive detection of low-level muscle fatigue using surface emg with wavelet decomposition. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2018 , 5648-5651. doi: 10.1109/EMBC.2018.8513588
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dc.coverage.spatial.spa.fl_str_mv Cundinamarca
dc.coverage.temporal.spa.fl_str_mv 2023
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dc.publisher.grantor.spa.fl_str_mv Universidad Militar Nueva Granada
institution Universidad Militar Nueva Granada
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spelling Rubiano Fonseca, AstridRamirez Hernandez, Juan DavidIngeniero en MecatrónicaRamirez, Jose LuisCundinamarca2023Campus UMNG2025-11-06T16:50:46Z2025-11-06T16:50:46Z2024-04-13https://hdl.handle.net/10654/47625instname:Universidad Militar Nueva Granadareponame:Repositorio Institucional Universidad Militar Nueva Granadarepourl:https://repository.umng.edu.coEn el presente documento se presenta el desarrollo de un modelo de predicción de fatiga muscular en el antebrazo, basado en señales de electromiografía superficial, para la adquisición de estas señales será utilizado el dispositivo Myo el cual cuenta con 8 electrodos integrados y un protocolo de comunicación Bluetooth. Para esto se propone un análisis de las características como Entropía (H), Derivada de la Entropía ( ̇H), valor eficaz (RMS), Valor absoluto medio (MAV) y Varianza (VAR). Se determina la linealidad del sistema por la distancia euclidiana entre los espacios de características, posteriormente se alimenta un sistema SVM lineal para la segmentación de los grupos de datos. Finalmente, los resultados obtenidos permitieron el desarrollo de un modelo predictivo de fatiga muscular con una predicción asertiva del 79.3 % con una validación cruzada, y se obtuvieron resultados de 87.5 % y 91.7 % de predicción asertiva en las pruebas del modelo entrenado.In this document, the development of a prediction model for muscular fatigue in the forearm is presented. This model is based on surface electromyography signals, and for signal acquisition, the Myo device will be used, which has 8 integrated electrodes and a Bluetooth communication protocol. An analysis of features such as Entropy (H), Entropy Derivative ( ̇H), Root Mean Square (RMS), Mean Absolute Value (MAV), and Variance (VAR) is proposed. The linearity of the system is determined by the Euclidean distance between feature spaces. Subsequently, a linear SVM system is fed for data group segmentation. Finally, the results obtained allowed the development of a predictive model for muscular fatigue with an assertive prediction of 79.3% with cross-validation. Additionally, assertive prediction results of 87.5% and 91.7% were obtained in tests of the trained model.Introducción Planteamiento del problema Alcance o delimitación de la propuesta Objetivos Estudio del estado del arte Justificación Marco referencial Metodología Materiales y métodos Modelo predictivo Resultados experimentales Discusión Conclusiones ReferenciasPregradoapplicaction/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalAcceso abiertohttp://purl.org/coar/access_right/c_abf2Desarrollo de un modelo de fatiga muscular por EMG para jóvenes tenistasDevelopment of a muscle fatigue model using EMG for young tennis players.Tesis/Trabajo de grado - Monografía - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fIngeniería en MecatrónicaFacultad de IngenieríaUniversidad Militar Nueva Granadabalandinodidonato. (2016). Myotoolkit/software for thalmic’s myo armband.md at master · balandinodidonato/myotoolkit · github. Descargado de https://github.com/balandinodidonato/MyoToolkit/blob/master/Software%20for%20Thalmic%27s%20Myo%20armband.mdBoon-Leng, L., Dae-Seok, L., y Boon-Giin, L. (2016, 1). Mobile-based wearable-type of driver fatigue detection by gsr and emg. IEEE Region 10 Annual International Conference, Proceedings/TENCON , 2016-January. doi: 10.1109/TENCON.2015.7372932Castiblanco, P. A., Ramirez, J. L., y Rubiano, A. (2021). Smart materials and their application in robotic hand systems: A state of the art. Indonesian Journal of Science and Technology, 6 , 401-426. Descargado de https://ejournal.upi.edu/index.php/ijost/article/view/35630 doi: 10.17509/IJOST.V6I2.35630Cutts, S., Gangoo, S., Modi, N., y Pasapula, C. (2019, 1). Tennis elbow: A clinical review article. Journal of orthopaedics, 17 , 203-207. Descargado de https://pubmed.ncbi.nlm.nih.gov/31889742/ doi: 10.1016/J.JOR.2019.08.005Furui, A., y Tsuji, T. (2019, 7). Muscle fatigue analysis by using a scale mixture-based stochastic model of surface emg signals. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , 1948-1951. doi: 10.1109/EMBC.2019.8856348Girard, O., y egoire Millet, G. P. (2009). Neuromuscular fatigue in racquet spor ts. PhysMed Rehabil Clin N Am, 26 , 161-173. doi: 10.1016/j.pmr.2008.10.008Jero, S. E., y Ramakrishnan, S. (2019, 7). Analysis of muscle fatigue conditions in surface emg signal with a novel hilbert marginal spectrum entropy method. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS , 2675-2678. doi: 10.1109/EMBC.2019.8857077Jinpyeo Jeung, Y. C. I. Y. (2020). Quantitative muscle fatigue estimation with high snr flexible skin electrode. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). doi: 10.0/Linux-x86_64Khanam, F., y Ahmad, M. (2016, 3). Frequency based emg power spectrum analysis of salat associated muscle contraction. ICEEE 2015 - 1st International Conference on Electrical and Electronic Engineering, 161-164. doi: 10.1109/CEEE.2015.7428245Labs, T. (2020). Thalmic labs · github. Descargado de https://github.com/thalmiclabsLarson, D. J., y Brown, S. H. (2022, 3). Influence of back muscle fatigue on dynamic lumbar spine stability and coordination variability of the thorax-pelvis during repetitive flexion-extension movements. Journal of biomechanics, 133 . Descargado de https://pubmed.ncbi.nlm.nih.gov/35081464/ doi: 10.1016/J.JBIOMECH.2022.110959McDonald, A. C., Mulla, D. M., y Keir, P. J. (2019, 2). Muscular and kinematic adaptations to fatiguing repetitive upper extremity work. Applied Ergonomics, 75 , 250-256. doi: 10.1016/J.APERGO.2018.11.001Na, Y., Lee, H.-D., y Kim, J. (2015). Muscle fatigue estimation with twitch force derived from semg peaks. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi: 10.1109/EMBC.2015.7319145Nathalli G. Readi, A. B. T. M. V., Lorenzo Schwarcke. (2015). The effect of lymph drainage on the myoelectric manifestation of vastus lateralis fatigue: Preliminary results. doi: 10.1109/EMBC.2015.7319923Ping, N., y Yang, J. (2022, 5). Exercise fatigue injury under sport resistance. Revista Brasileira de Medicina do Esporte, 28 , 682-685. Descargado de https://www.scielo.br/j/rbme/a/8sK3DjCHRkTMDYxVfZWTZXQ/ doi: 10.1590/1517-8692202228062022_0088Qi, M. S., Yang, W. J., Xie, P., Liu, Z. J., Zhang, Y. Y., y Cheng, S. C. (2019, 1). Driver fatigue assessment based on the feature fusion and transfer learning of eeg and emg. Proceedings 2018 Chinese Automation Congress, CAC 2018 , 1314-1317. doi: 10.1109/CAC.2018.8623087Rubiano, A., Ramirez, J. L., y Moreno, R. J. (2019). Comparison of features for semg based detection of hand movement inception using a wearable device. , 14 .Rubiano, A., Ramirez, J. L., Rubiano, A., y Ramirez, J. L. (2019, 3). Grasping recognition based on electromyographic signals using a wearable device. International Review of Automatic Control (IREACO), 12 , 95-101. Descargado de https://www.praiseworthyprize.org/jsm/ index.php?journal=ireaco&page=article&op=view&path[]=23076 doi: 10.15866/IREACO.V12I2.16267Sasidharan, D., y Venugopal, G. (2020, 7). Analysis of surface emg signals under fatigue conditions using sum of sines models. Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020 , 849-852. doi: 10.1109/ICCSP48568.2020.9182049Zhang, G., Morin, E., Zhang, Y., y Etemad, S. A. (2018, 7). Non-invasive detection of low-level muscle fatigue using surface emg with wavelet decomposition. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2018 , 5648-5651. doi: 10.1109/EMBC.2018.8513588FatigaEpicondilitisSeñalesBiomecanicaEntropiaEspacio de estadosSVMElectromiografiaMuscle FatigueEpicondylitisSignalsBiomechanicsEntropyState SpaceSVM (Support Vector Machine)ElectromyographyORIGINALRamirezHernandezJuanDavid2024.pdfRamirezHernandezJuanDavid2024.pdfTrabajo de gradoapplication/pdf8384824https://repository.umng.edu.co/bitstreams/e8513def-b679-4232-9beb-d7b43d5a81b0/downloadde25087da65504fd3c18ce96ae386376MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83420https://repository.umng.edu.co/bitstreams/3e39b7e7-50c0-49ec-bd6d-7b4122f82bab/downloada609d7e369577f685ce98c66b903b91bMD52THUMBNAILRamirezHernandezJuanDavid2024.pdf.jpgRamirezHernandezJuanDavid2024.pdf.jpgIM Thumbnailimage/jpeg6351https://repository.umng.edu.co/bitstreams/544c6780-3460-4c10-b398-c6a415931ad3/download21dff2f1b35cd5385b1a1c501c086b7eMD5310654/47625oai:repository.umng.edu.co:10654/476252025-11-08 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