Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX

El accidente cerebrovascular es la segunda causa principal de muerte y la tercera de discapacidad, y el 75% de las personas que sufren un accidente cerebrovascular cada año experimentan limitaciones en la movilidad relacionadas con la marcha. Se han considerado estrategias que involucran dispositivo...

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Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/31583
Acceso en línea:
https://doi.org/10.48713/10336_31583
https://repository.urosario.edu.co/handle/10336/31583
Palabra clave:
Sensor EMG
Sensor IMU
Umbral
Intención de movimiento
Extracción de características estadísticas
Ciencias médicas, Medicina
Ingeniería & operaciones afines
EMG sensor
IMU sensor
Threshold
Movement intention
Statistical features extraction
Diseño en ingeniería
Electromiografía
Rehabilitación médica
Trastornos del movimiento
Biosensores
Rights
License
Atribución-NoComercial-SinDerivadas 2.5 Colombia
id EDOCUR2_138aad3ef36fda4375b6f7f19b0c23cf
oai_identifier_str oai:repository.urosario.edu.co:10336/31583
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.spa.fl_str_mv Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
dc.title.TranslatedTitle.spa.fl_str_mv Desarrollo de una interfaz para la rehabilitación basada en la señal de EMG para el control del exoesqueleto de tobillo T-FLEX
title Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
spellingShingle Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
Sensor EMG
Sensor IMU
Umbral
Intención de movimiento
Extracción de características estadísticas
Ciencias médicas, Medicina
Ingeniería & operaciones afines
EMG sensor
IMU sensor
Threshold
Movement intention
Statistical features extraction
Diseño en ingeniería
Electromiografía
Rehabilitación médica
Trastornos del movimiento
Biosensores
title_short Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
title_full Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
title_fullStr Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
title_full_unstemmed Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
title_sort Development of an interface for rehabilitation based on the EMG signal for the control of the ankle exoskeleton T-FLEX
dc.contributor.advisor.none.fl_str_mv Múnera Ramirez, Marcela Cristina
Cifuentes García, Carlos Andrés
dc.subject.spa.fl_str_mv Sensor EMG
Sensor IMU
Umbral
Intención de movimiento
Extracción de características estadísticas
topic Sensor EMG
Sensor IMU
Umbral
Intención de movimiento
Extracción de características estadísticas
Ciencias médicas, Medicina
Ingeniería & operaciones afines
EMG sensor
IMU sensor
Threshold
Movement intention
Statistical features extraction
Diseño en ingeniería
Electromiografía
Rehabilitación médica
Trastornos del movimiento
Biosensores
dc.subject.ddc.spa.fl_str_mv Ciencias médicas, Medicina
Ingeniería & operaciones afines
dc.subject.keyword.spa.fl_str_mv EMG sensor
IMU sensor
Threshold
Movement intention
Statistical features extraction
dc.subject.lemb.spa.fl_str_mv Diseño en ingeniería
Electromiografía
Rehabilitación médica
Trastornos del movimiento
Biosensores
description El accidente cerebrovascular es la segunda causa principal de muerte y la tercera de discapacidad, y el 75% de las personas que sufren un accidente cerebrovascular cada año experimentan limitaciones en la movilidad relacionadas con la marcha. Se han considerado estrategias que involucran dispositivos robóticos, como exoesqueletos y órtesis, para mejorar la rehabilitación del accidente cerebrovascular. Algunos de ellos han incluido la implementación de señales de Electromiografía (EMG) ya sea para análisis de activación muscular o detección de intención de movimiento. Este último ha estado involucrado en el proceso de activación de dispositivos robóticos para manejar la asistencia del dispositivo por la intención del sujeto de realizar un movimiento específico. Esto permitiría al sujeto involucrarse en su terapia. Por lo tanto, este proyecto introduce una interfaz EMG para el control del exoesqueleto del tobillo T-FLEX. Se revisaron algunos estudios donde se han incluido señales EMG en los procesos de control y terapia, y se analizaron algoritmos con diferentes métodos de cálculo de umbral. Teniendo en cuenta la información de esos estudios, se desarrolló un algoritmo basado en umbrales para la detección de la intención de movimiento. El algoritmo constaba de dos etapas principales, el cálculo del umbral y la detección de la intención del movimiento. La primera etapa consistió en el establecimiento del umbral a través de la extracción de características estadísticas (MEAN, desviación estándar (STD), varianza (VAR), MEAN + 3 * STD y Root Mean Square value (RMS)) de la señal de EMG. El segundo consistió en comparar la señal con el valor de referencia (umbral). Para probar el algoritmo se planificaron dos sesiones. En la primera sesión participaron diez sujetos sanos y su señal EMG se adquirió del músculo tibial anterior a través de un sensor muscular Myoware. Además, se colocó un sensor de Unidad de Medición Inercial (IMU) en la punta del pie de cada participante para adquirir la velocidad angular cuando se realizó la dorsiflexión del tobillo. Las señales de salida de ambos sensores se registraron y el procesamiento con el algoritmo se realizó off-line. La segunda sesión se realizó con el exoesqueleto de tobillo T-FLEX y un Juego Serio, implementando el algoritmo en tiempo real con una característica estadística seleccionada de la primera sesión como umbral. Se evaluó la detección del algoritmo EMG. También se evaluó el algoritmo que ya tenía T-FLEX para la detección de la intención de movimiento con el sensor IMU. Los resultados de la primera sesión mostraron que la característica de MEAN funcionó para el establecimiento del umbral con el sensor IMU, y para el sensor EMG fue (VAR), presentando un error menor al 10% en la cantidad de Falsos Positivos (FP) y Falsos Negativos (FN). Con esto, se llevó a cabo la segunda sesión, demostrando que había más precisión en el manejo del juego usando el sensor IMU que el sensor EMG. Con el sensor EMG la máxima precisión alcanzada fue del 89,7% y con el sensor IMU fue del 94,1%.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-04T21:19:30Z
dc.date.available.none.fl_str_mv 2021-06-04T21:19:30Z
dc.date.created.none.fl_str_mv 2021-05-27
dc.type.eng.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.spa.fl_str_mv Trabajo de grado
dc.type.spa.spa.fl_str_mv Trabajo de grado
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48713/10336_31583
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/31583
url https://doi.org/10.48713/10336_31583
https://repository.urosario.edu.co/handle/10336/31583
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Atribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
dc.rights.uri.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/co/
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 2.5 Colombia
Abierto (Texto Completo)
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
http://purl.org/coar/access_right/c_abf2
dc.format.extent.spa.fl_str_mv 86
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad del Rosario
dc.publisher.department.spa.fl_str_mv Escuela de Medicina y Ciencias de la Salud
dc.publisher.program.spa.fl_str_mv Ingeniería Biomédica
institution Universidad del Rosario
dc.source.bibliographicCitation.spa.fl_str_mv W. Johnson, O. Onuma, M. Owolabi, and S. Sachdev,Stroke: a global response isneeded, 2016.
H. Mamoru Yoshida, J. Barreira, and P. Teixeira Fernandes, “Motor skills, depressivesymptoms and cognitive functions in post-stroke patients,”Fisioter Pesqui, vol. 26,no. 1, pp. 9–14, 2019.
R. Boian, J. E. Deutsch, and J. L. Grigore C. Burdea, “Post-Stroke Rehabilitation withthe Rutgers Ankle System: A Case Study,”PRESENCE, vol. 10, no. 4, pp. 416–430,2001.
F. A. Silva, J. G. Zarruk, C. Quintero, W. Arenas, C. F. Rueda-Clausen, S. Y. Silva,and A. M. Estupiñán, “Cerebrovascular disease in Colombia,”Revista Colombiana deCardiología, vol. 13, no. 2, 2006.
A. A. Duque and D. I. Lucumi,Caracterización del accidente cerebrovascular enColombia, 2019.
C. Camejo, C. Legnani, A. Gaye, B. Arcieri, F. Brumett, L. Castro, A. Peña, F. Gómez,J. R. Higgie, F. Preve, and R. Salamano, “Stroke Unit at the Hospital de Clínicas:clinical-epidemiological behavior in patients with stroke (2007-2012),”Archivos deMedicina Interna, vol. 37, no. 1, 2015.
L. W. Jian and S. N. W. Shamsuddin, “The design of virtual lower limb rehabilitation for post-stroke patients,”Indonesian Journal of Electrical Engineering and ComputerScience, vol. 16, no. 1, pp. 544–552, 2019.
R. Jiménez-Fabián and O. Verlinden, “Review of control algorithms for robotic anklesystems in lower-limb orthoses, prostheses, and exoskeletons,”Medical Engineering &Physics, vol. 34, pp. 397–408, 2012.
J. Toglia, G. Askin, L. M. Gerber, A. Jaywant, and M. W. O’Dell, “Participationin Younger and Older Adults Post-stroke: Frequency, Importance, and Desirability ofEngagement in Activities,”Frontiers in Neurology, 2019.
V. Demarin, S. Morović, and R. BéNé, “Neuroplasticity,”Periodicum Biologorum,vol. 116, no. 2, pp. 209–211, 2014.
A. V. Zakharov, V. A. Bulanov, E. V. Khivintseva, A. V. Kolsanov, Y. V. Bushkova,and G. E. Ivanova, “Stroke Affected Lower Limbs Rehabilitation Combining VirtualReality With Tactile Feedback,”Frontiers in Robotics and AI, 2020.54
F. M. Lim, R. Foong, K. S. Goh, Q. L. Mok, B. H. J. Tan, S. L. Toh, and H. Yu,“Supine Gait Training Device for Stroke Rehabilitation – Design of a Compliant AnkleOrthosis,”IFMBE Proceedings, vol. 43, pp. 512–513, 2014.
A. Esquenazi, S. Lee, A. Wikoff, A. Packel, T. Toczylowski, and J. Feeley, “A Compar-ison of Locomotor Therapy Interventions: Partial-Body WeightLSupported Treadmill,Lokomat, and G-EO Training in People With Traumatic Brain Injury,”AmericanAcademy of Physical Medicine and Rehabilitation, vol. 9, pp. 839–846,
J. Fang, A. Vuckovic, S. Galen, C. Cossar, B. A. Conway, and K. J. Hunt, “Design andevaluation of a prototype gait orthosis for early rehabilitation of walking,”Technologyand Health Care, vol. 22, pp. 273–288, 2014.
F. M. Alfieri, C. d. S. Dias, A. C. A. dos Santos, and L. R. Battistella, “Acute Effectof Robotic Therapy (G-EO System) on the Lower Limb Temperature Distribution of aPatient with Stroke Sequelae,”Case Reports in Neurological Medicine, vol. 1, pp. 2–5,2019.
J. Stein, R. Hughes, S. E. Fasoli, H. I. Krebs, and N. Hogan, “Technological Aids for Motor Recovery,” in Stroke Recovery and Rehabilitation, 2020, pp. 307–321.
Y. Lee, J. G. Her, Y. Choi, and H. Kim, “Effect of ankle-foot orthosis on lower limb muscle activities and static balance of stroke patients authors’ names,” Journal of Physical Therapy Science, vol. 26, no. 2, pp. 179–182, 2014, issn: 09155287. doi: 10.1589/jpts.26.179.
M. Sartori, M. Reggiani, C. Mezzato, and E. Pagello, “A Lower Limb EMG-driven Biomechanical Model for Applications in Rehabilitation Robotics,” IEEE, pp. 1–7, 2009.
J. C. Moreno, J. Figueiredo, and J. L. Pons, “Exoskeletons for lower-limb rehabilita tion,” in Rehabilitation Robotics. 2018, pp. 89–99.
W. Alcocer, L. Vela, A. Blanco, J. Gonzalez, and M. Oliver, “Mayor Trends in the Development of Ankle Rehabilitation Devices,” DYNA, vol. 79, no. 176, 2012.
Y. Ganesan, S. Gobee, and V. Durairajah, “Development of an Upper Limb Exoskele ton for Rehabilitation with Feedback from EMG and IMU Sensor,” Procedia Computer Science, vol. 76, no. 1, pp. 53–59, 2015.
C. A. Aguilar Lazcano, O. O. Sandoval Gonzalez, J. A. Diaz Reyes, I. Herrera Aguilar, and A. Martínez Sibaja, Sistema de reconocimiento de gestos utilizando señales EMG para aplicaciones de control de prótesis biónicas y exoesqueletos de mano, 2016.
K. Chen, B. Xiong, Y. Ren, A. Y. Dvorkin, D. Gaebler-Spira, C. E. Sisung, and L.-Q. Zhang, “Ankle Passive and Active Movement Training in Children with Acute Brain Injury Using a Wearable Robot,” J Rehabil Med, vol. 50, pp. 30–36, 2018.
A. Jaume-i-Capó, J. V. Gomez, G. Moyà, and F. Perales, “Motivational Rehabilitation using Serious Games,” VAR., vol. 4, no. 9, pp. 167–173, 2013. 55
A. Pino, D. Gomez-Vargas, M. Munera, and C. A. Cifuentes, “Visual Feedback Strategy based on Serious Games for Therapy with T-FLEX Ankle Exoskeleton,” The Interna tional Symposium on Wearable Robotics (WeRob2020) and WearRAcon Europe,, pp. 1– 2, 2020.
N. R. García, “Diseño y Evaluación Ergonómica de Interfaces Físicas para la Órtesis Robótica de Tobillo (T-FLEX) a través de la Integración de Superficies Blandas,” 2019.
D. Gomez-Vargas, F. Ballen-Moreno, P. Barria, R. Aguilar, J. M. Azorín, M. Munera, and C. A. Cifuentes, “Actuation system of the ankle exoskeleton T-FLEX: first use experimental validation in people with stroke,” Tech. Rep., pp. 1–17.D. Gomez-Vargas, M. J. Pinto-Bernal, F. Ballén-Moreno, M. Múnera, and C. A. Ci fuentes, “Therapy with T-FLEX Ankle-Exoskeleton for Motor Recovery:A Case Study with a Stroke Survivor,” in T-FLEX, 2020, pp. 1–6.
M. Manchola, D. Serrano, D. Gómez, F. Ballen, D. Casas, M. Munera, and C. A. Cifuentes, “T-FLEX: Variable Stiffness Ankle-Foot Orthosis for Gait Assistance,” in Wearable Robotics: Challenges and Trends, 2018, pp. 160–164.
C. L. Brockett and G. J. Chapman, “Biomechanics of the ankle,” Orthopaedics and Trauma, vol. 30, no. 3, pp. 232–238, 2016.
J.-W. Lee, S.-W. Yoon, J.-H. Kim, Y.-P. Kim, and Y.-N. Kim, “The Effect of the Ankle Range of Motion on Balance Performance of Elderly People,” J.Phys. Ther. Sci, vol. 24, pp. 991–994, 2012.
F. Gao, T. Tian, T. Yao, and Q. Zhang, “Human Gait Recognition Based on Mul tiple Feature Combination and Parameter Optimization Algorithms,” Computational Intelligence and Neuroscience, pp. 1–14, 2021.
P. Aleixoa, J. V. Patto, A. Cardoso, H. Moreira, and J. Abrantes, “Ankle kinematics and kinetics during gait in healthy and rheumatoid arthritis post-menopausal women,” Somatosensory & Motor Research, vol. 36, no. 2, pp. 171–178, 2019.
B. M. Logan, “Muscles,” in McMinn’s Color Atlas of Foot and Ankle Anatomy, 2012, pp. 116–121.
S.-W. Yoon, J.-W. Lee, Y.-N. Kim, Y.-S. Kim, W.-S. Cho, and C.-B. Park, “Change in Ankle Dorsiflexion Range of Motion and Ultrasonographic Images of the Tibialis Anterior with Age,” J.Phys. Ther. Sci, vol. 23, pp. 813–815, 2011.
S. Dorsch, L. Ada, C. G. Canning, M. Al-Zharani, and C. Dean, “The Strength of the Ankle Dorsiflexors Has a Significant Contribution to Walking Speed in People Who Can Walk Independently After Stroke: An Observational Study,” Arch Phys Med Rehabil, vol. 93, pp. 1072–2076, 2012.
B. F. Koseoglu, A. Dogan, H. U. Tatli, D. S. Ozcan, and C. S. Polat, “Can kinesio tape be used as an ankle training method in the rehabilitation of the stroke patients?” Complementary Therapies in Clinical Practice, vol. 27, pp. 46–51, 2017. 56
D. H. Richie, “Functional Instability of the Ankle and the Role of Neuromuscular Control: A Comprehensive Review,” The Journal of Foot and Ankle Surgery, vol. 40, no. 4, pp. 240–251, 2001.
P. S. Karakkattil, E. Trudelle-Jackson, A. Medley, and C. Swank, “Effects of two different types of ankle–foot orthoses on gait outcomes in patients with subacute stroke: a randomized crossover trial.,” Clinical Rehabilitation, vol. 34, no. 8, pp. 1094–1102, 2020.
L. W. Forrester, A. Roy, R. N. Goodman, J. Rietschel, J. E. Barton, H. I. Krebs, and R. F. Macko, “Clinical application of a modular ankle robot for stroke rehabilitation,” NeuroRehabilitation, vol. 33, pp. 57–97, 2013.
L. R. Sheffler and J. Chae, “Technological Advances in Interventions to Enhance Post stroke Gait,” Phys Med Rehabil Clin N Am, vol. 24, pp. 305–323, 2013.
E. Zimmerman, G. Carnaby, C. L. Lazarus, and G. A. Malandraki, “Motor Learn ing, Neuroplasticity, and Strength and Skill Training: Moving From Compensation to Retraining in Behavioral Management of Dysphagia,” American Journal of Speech Language Pathology, vol. 29, pp. 1065–1077, 2020.
L. Chen, S. Xiong, Y. Liu, M. Lin, L. Zhu, R. Zhong, J. Zhao, W. Liu, J. Wang, and X. Shang, “Comparison of Motor Relearning Program versus Bobath Approach for Prevention of Poststroke Apathy: A Randomized Controlled Trial,” Journal of Stroke and Cerebrovascular Diseases, vol. 28, no. 3, pp. 655–664, 2019.
P. Péran, F. Nemmi, C. Dutilleul, L. Finamore, C. F. Caravasso, E. Troisi, M. Iosa, U. Sabatini, and M. G. Grasso, “Neuroplasticity and brain reorganization associated with positive outcomes of multidisciplinary rehabilitation in progressive multiple sclerosis: A fMRI study,” Multiple Sclerosis and Related Disorders, vol. 42, pp. 1–7, 2020.
J. Jiang, K.-M. Lee, and J. Ji, “Review of anatomy-based ankle–foot robotics for mind, motor and motion recovery following stroke: design considerations and needs,” International Journal of Intelligent Robotics and Applications, pp. 1–16, 2018.
D. Park, H.-S. Cynn, C. Yi, W. J. Choi, J. Shim, and D.-W. Oh, “Four-week training involving self-ankle mobilization with movement versus calf muscle stretching in patients with chronic stroke: a randomized controlled study,” Topics in Stroke Rehabilitation, pp. 1–9, 2019.
L.-F. Yeung, C. C. Y. Lau, C. W. K. Lai, Y. O. Y. Soo, M.-L. Chan, and R. K. Y. Tong, “Effects of wearable ankle robotics for stair and over-ground training on sub acute stroke: a randomized controlled trial,” J NeuroEngineering Rehabil, vol. 18, no. 19, pp. 1–10, 2021.
G. Chen, P. Qi, Z. Guo, and H. Yu, “Mechanical design and evaluation of a com pact portable knee–ankle–foot robot for gait rehabilitation,” Mechanism and Machine Theory, vol. 103, pp. 51–64, 2016.
Q. Liu, C. Wang, J. J. Long, T. Sun, L. Duan, X. Zhang, B. Zhang, Y. Shen, W. Shang, Z. Lin, Y. Wang, J. Xia, J. Wei, W. Li, and Z. Wu, “Development of a New Robotic Ankle Rehabilitation Platform for Hemiplegic Patients after Stroke,” Journal of Healthcare Engineering, pp. 1–12, 2018. 57
R. N. Goodman, J. C. Rietschel, A. Roy, B. C. Jung, J. Diaz, R. F. Macko, and L. W. Forrester, “Increased reward in ankle robotics training enhances motor control and cortical efficiency in stroke,” JRRD, vol. 51, no. 2, pp. 213–227, 2014.
S. M. S. Hasan, M. R. Siddiquee, R. Atri, R. Ramon, J. S. Marquez, and O. Bai, “Prediction of gait intention from pre-movement EEG signals: a feasibility study,” Journal of NeuroEngineering and Rehabilitation, vol. 17, no. 50, pp. 1–16, 2020.
J. Lobo-Prat, P. N. Kooren, A. H. Stienen, J. L. Herder, B. F. Koopman, and P. H. Veltink, “Non-invasive control interfaces for intention detection in active movement assistive devices,” Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 168, pp. 1–22, 2014.
J. de Vries, A. van Ommeren, G. Prange-Lasonder, J. Rietman, and P. Veltink, “De tection of the intention to grasp during reach movements,” Journal of Rehabilitation and Assistive Technologies Engineering, vol. 5, pp. 1–9, 2017.
E. Wentink, S. Beijen, H. Hermens, J. Rietman, and P. Veltink, “Intention detection of gait initiation using EMG and kinematic data,” Gait & Posture, vol. 37, pp. 223–228, 2013.
F. Feuvrier, B. Sijobert, C. Azevedo, K. Griffiths, S. Alonso, A. Dupeyron, I. Laffont, and J. Froger, “Inertial measurement unit compared to an optical motion capturing system in post-stroke individuals with foot-drop syndrome,” Annals of Physical and Rehabilitation Medicine, vol. 63, pp. 195–201, 2020.
I.-S. Weon and S.-G. Lee, “Intelligent robotic walker with actively controlled human interaction,” Wiley Etri Journal, vol. 40, no. 4, pp. 522–530, 2018.
D. Novak, P. Rebersek, S. Marco, M. D. Rossi, M. Donati, J. Podobnik, T. Beravs, T. Lenzi, N. Vitiello, M. C. Carrozza, and M. Munih, “Automated detection of gait initiation and termination using wearable sensors,” Medical Engineering & Physics, vol. 35, pp. 1713–1720, 2013.
V. Hernandez, D. Dadkhah, V. Babakeshizadeh, and D. Kulic, “Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach,” Gait & Posture, vol. 83, pp. 185–193, 2021.
E. Wentink, V. Schut, E. Prinsen, J. Rietman, and P. Veltink, “Detection of the onset of gait initiation using kinematic sensors and EMG in transfemoral amputees,” Gait & Posture, vol. 39, pp. 391–396, 2014.
S. A. Khomami and S. Shamekhi, “Persian sign language recognition using IMU and surface EMG sensors,” Measurement, vol. 168, pp. 1–9, 2021.
D. Buongiorno, M. Barsotti, F. Barone, V. Bevilacqua, and A. Frisoli, “A Linear Ap proach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Inten tion Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints,” Front. Neurorobot, vol. 12, pp. 1–12, 2018. 58
W. Hassani, S. Mohammed, H. Rifaï, and Y. Amirat, “Powered orthosis for lower limb movements assistance and rehabilitation,” Control Engineering Practice, vol. 26, pp. 245–253, 2014.
A. Avila and J.-Y. Chang, “EMG onset detection and upper limb movements identifi cation algorithm,” Microsyst Technol, vol. 20, pp. 1635–1640, 2014.
D.-H. Moon, D. Kim, and Y.-D. Hong, “Intention Detection Using Physical Sensors and Electromyogram for a Single Leg Knee Exoskeleton,” Sensors, vol. 19, no. 4447, pp. 1–15, 2019.
Y. Yin, “Estimation of Skeletal Muscle Activation and Contraction Force Based on EMG Signals,” in Biomechanical Principles on Force Generation and Control of Skele tal Muscle and their Applications in Robotic Exoskeleton, 2020, pp. 91–114.
C. A. Quinayás-Burgos and C. A. Gaviria-López, “Movement intention detection sys tem for myoelectric control of a prosthetic robotic hand,” Ing. Univ, vol. 19, no. 1, pp. 27–50, 2015.
F. Astudillo, J. Charry, I. Minchala, and S. Wong, “Lower limbs motion intention detection by using pattern recognition,” in Lower limbs motion intention detection by using pattern recognition, 2019, pp. 1–6.
T. Cao, D. Liu, Q. Wang, O. Bai, and J. Sun, “Surface Electromyography-Based Action Recognition and Manipulator Control,” applied sciences, vol. 10, no. 5823, pp. 1–20, 2020.
G. J. Androwis, R. Pilkar, A. Ramanujam, and K. J. Nolan, “Electromyography As sessment During Gait in a Robotic Exoskeleton for Acute Stroke,” Front. Neurol., vol. 9, no. 630, 2018.
S. Y. Gordleeva, S. A. Lobov, N. A. Grigorev, A. O. Savosenkov, M. O. Shamshin, M. V. Lukoyanov, M. A. Khoruzhko, and V. B. Kazantsev, “Real-Time EEG–EMG Hu man–Machine Interface-Based Control System for a Lower-Limb Exoskeleton,” IEEE ACCESS, vol. 8, pp. 84 070–84 081, 2020.
S. Peters, T. Ivanova, B. Lakhani, L. A. Boyd, and S. J. Garland, “Neuroplasticity of Cortical Planning for Initiating Stepping Poststroke: A Case Series,” Journal of Neurologic Physical Therapy, vol. 44, no. 2, pp. 164–172, 2020.
M. Li, G. Xu, J. Xie, and C. Chen, “A review: Motor rehabilitation after stroke with control based on human intent,” Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine, pp. 1–62, 2018.
N. Irastorza-Landa, A. Sarasola-Sanz, E. López-Larraz, C. Bibián, F. Shiman, N. Bir baumer, and A. Ramos-Murguialday, “Design of Continuous EMG Classification ap proaches towards the Control of a Robotic Exoskeleton in Reaching Movements,” IEEE International Conference on Rehabilitation Robotics, 2017.
M. Ozsert, O. Yavuz, and L. Durak-Ata, “Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms,” Com puter Methods in Biomechanics and Biomedical Engineering, vol. 14, no. 6, pp. 521– 525, 2011. 59
C. Liu, L. Pan, Z. Gu, J. Wang, Y. Ren, and Z. Wang, “Valid Probabilistic Anomaly Detection Models for System Logs,” Wireless Communications and Mobile Computing, pp. 1–12, 2020.
J. Mickelborough, M. L. van der Linden, R. C. Tallis, and A. R. Ennos, “Muscle activity during gait initiation in normal elderly people,” Gait & Posture, vol. 19, no. 1, pp. 50– 57, 2004.
L. Hardesty, Explained: Neural networks, 2017.
C. Lersviriyanantakul, A. Booranawong, K. Sengchuai, P. Phukpattaranont, B. Wongkittisuksa, and N. Jindapetch, “Implementation of a Real-Time Automatic Onset Time Detection for Surface Electromyography Measurement Systems Using NI myRio,” Thermal Science, vol. 20, no. 2, S591–S602, 2016.
M. Tabie and E. A. Kirchner, EMG Onset Detection Comparison of different methods for a movement prediction task based on EMG, 2013.
A. Avila and J.-Y. Chang, “EMG onset detection and upper limb movements identifi cation algorithm,” Microsyst Technol, vol. 20, pp. 1635–1640, 2014.
A. Kontunen, V. Rantanen, A. Vehkaoja, M. Ilves, J. Lylykangas, E. Makela, M. Rautiainen, V. Surakka, and J. Lekkala, “Low-latency EMG Onset and Termination Detection for Facial Pacing,” in IFMBE proceedings, 2018, pp. 1016–1019.
J.-H. Park, H.-S. Moon, H. Kim, and S.-T. Chung, “Detection of Movement Intention for Operating Methods of Serious Games,” Appl. Sci., vol. 11, no. 883, pp. 1–14, 2021.
S.-N. Jeon and J.-H. Choi, “The effects of ankle joint strategy exercises with and without visual feedback on the dynamic balance of stroke patients,” J Phys Ther Sci, vol. 27, no. 8, 2015.
C. Prahm, I. Vujaklija, F. Kayali, P. Purgathofer, and O. C. Aszmann, “Game-Based Rehabilitation for Myoelectric Prosthesis Control,” Jmir Serious Games, vol. 5, no. 1, pp. 1–13, 2017.
R. de la Rosa, A. Alonso, S. de la Rosa, and D. Abásolo, “Myo-Pong: a neuromuscular game for the UVa-Neuromuscular Training System Platform,” IEEE, p. 61, 2008.
Á. Gutiérrez, D. Sepúlveda-Muñoz, Á. Gil-Agudo, and A. d. l. R. Guzmán, “Serious Game Platform with Haptic Feedback and EMG Monitoring for Upper Limb Reha bilitation and Smoothness Quantification on Spinal Cord Injury Patients,” Appl. Sci., vol. 10, no. 963, pp. 1–17, 2020.
L. van Dijk, C. K. van der Sluis, H. W. van Dijk, and R. M. Bongers, “Learning an EMG Controlled Game: TaskSpecific Adaptations and Transfer,” Plos One, vol. 11, no. 8, pp. 1–14, 2016.
R. Labruyére, C. N. Gerber, K. Birrer-Brutsch, A. Meyer-Heim, and H. J. van Hedel, “Requirements for and impact of a serious game for neuro-pediatric robot-assisted gait training,” Hubertus J.A. van Hedel, vol. 34, pp. 3906–3915, 2013.
J. C. Pérez-Ibarra and A. A. G. Siqueira, “Comparison of Kinematic and EMG parame ters between unassisted, fixed- and adaptive-stiffness robotic-assisted ankle movements in post-stroke subjects,” IEEE, pp. 461–466, 2017. 60
G. Serrano, Electromiografia, 2020.
A. P. Laquidara, L. M. Zerbino, C. Lagraña, and E. Yedinak, “Automioestimulador: Biofeedback Aplicado a la Rehabilitación,” Segundas Jornadas de Investigación y Transferencia, pp. 293–298, 2013.
Y. Lan, J. Yao, and J. Dewald P.A., “The Impact of Shoulder Abduction Loading on EMG-based Intention Detection of Hand Opening and Closing After Stroke,” Conf Proc IEEE Eng Med Biol Soc., p. 1, 2011.
A. Technologies, MyoWare™ Muscle Sensor (AT-04-001), 2015.
B. Sensortec, BNO005 Intelligent 9-axis absolute orientation sensor, 2020.
B. E. . Kaminski, Emg Circuit, 2015.
D. A. G. Vargas, “Development of control strategies for a variable stiffness ankle exoskeleton for gait rehabilitation,” Ph.D. dissertation, 2020, pp. 1–136.
A. D. P. López, “Development of a Serious Game for Ankle Rehabilitation with T FLEX,” Ph.D. dissertation, 2020, pp. 1–67.
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spelling Múnera Ramirez, Marcela Cristina5696993b-4315-49f2-b8ca-139c129d4b75600Cifuentes García, Carlos Andréscd73ee3d-22fa-49f9-9c45-6092ca641c4c600Castellanos Guarnizo, Camila AndreaIngeniero BiomédicoFull timedbb8950e-0a5d-4273-9e0f-3afbb05c2e566002021-06-04T21:19:30Z2021-06-04T21:19:30Z2021-05-27El accidente cerebrovascular es la segunda causa principal de muerte y la tercera de discapacidad, y el 75% de las personas que sufren un accidente cerebrovascular cada año experimentan limitaciones en la movilidad relacionadas con la marcha. Se han considerado estrategias que involucran dispositivos robóticos, como exoesqueletos y órtesis, para mejorar la rehabilitación del accidente cerebrovascular. Algunos de ellos han incluido la implementación de señales de Electromiografía (EMG) ya sea para análisis de activación muscular o detección de intención de movimiento. Este último ha estado involucrado en el proceso de activación de dispositivos robóticos para manejar la asistencia del dispositivo por la intención del sujeto de realizar un movimiento específico. Esto permitiría al sujeto involucrarse en su terapia. Por lo tanto, este proyecto introduce una interfaz EMG para el control del exoesqueleto del tobillo T-FLEX. Se revisaron algunos estudios donde se han incluido señales EMG en los procesos de control y terapia, y se analizaron algoritmos con diferentes métodos de cálculo de umbral. Teniendo en cuenta la información de esos estudios, se desarrolló un algoritmo basado en umbrales para la detección de la intención de movimiento. El algoritmo constaba de dos etapas principales, el cálculo del umbral y la detección de la intención del movimiento. La primera etapa consistió en el establecimiento del umbral a través de la extracción de características estadísticas (MEAN, desviación estándar (STD), varianza (VAR), MEAN + 3 * STD y Root Mean Square value (RMS)) de la señal de EMG. El segundo consistió en comparar la señal con el valor de referencia (umbral). Para probar el algoritmo se planificaron dos sesiones. En la primera sesión participaron diez sujetos sanos y su señal EMG se adquirió del músculo tibial anterior a través de un sensor muscular Myoware. Además, se colocó un sensor de Unidad de Medición Inercial (IMU) en la punta del pie de cada participante para adquirir la velocidad angular cuando se realizó la dorsiflexión del tobillo. Las señales de salida de ambos sensores se registraron y el procesamiento con el algoritmo se realizó off-line. La segunda sesión se realizó con el exoesqueleto de tobillo T-FLEX y un Juego Serio, implementando el algoritmo en tiempo real con una característica estadística seleccionada de la primera sesión como umbral. Se evaluó la detección del algoritmo EMG. También se evaluó el algoritmo que ya tenía T-FLEX para la detección de la intención de movimiento con el sensor IMU. Los resultados de la primera sesión mostraron que la característica de MEAN funcionó para el establecimiento del umbral con el sensor IMU, y para el sensor EMG fue (VAR), presentando un error menor al 10% en la cantidad de Falsos Positivos (FP) y Falsos Negativos (FN). Con esto, se llevó a cabo la segunda sesión, demostrando que había más precisión en el manejo del juego usando el sensor IMU que el sensor EMG. Con el sensor EMG la máxima precisión alcanzada fue del 89,7% y con el sensor IMU fue del 94,1%.Stroke is the second leading cause of death and third of disability, and 75% of individuals who sustain a stroke each year experience limitations in mobility-related to walking. Strategies involving robotic devices, such as exoskeletons and orthoses, have been considered to improve stroke rehabilitation. Some of them have included the implementation of Electromyography(EMG) signals either for muscle activation analysis or movement intention detection. The latter has been involved in the activation process of robotic devices to handle the device’s assistance by the subject’s intention to perform a specific movement. This would allow the subject to get involved in his/her therapy. Hence, this project introduces an EMG interface for the control of the ankle exoskeleton T-FLEX. Some studies where EMG signals have been included in control and therapy processes were reviewed, and algorithms with different threshold methods calculation were analyzed. Considering the information from those studies, a threshold-based algorithm for movement intention detection was developed. The algorithm consisted in two main stages, the threshold calculation and the movement intention detection. The first stage consisted on the threshold establishment through statistical features extraction (MEAN, standard deviation (STD), variance (VAR), MEAN + 3*STD and Root Mean Square value (RMS)) from the EMG signal. The second consisted of comparing the signal with the reference value (threshold).To test the algorithm, two sessions were planned. In the first session, ten healthy subjects participated and their EMG signal was acquired from the Tibialis Anterior muscle through a Myoware muscle sensor. Additionally, an Inertial Measurement Unit (IMU) sensor was placed on each participant’s foot tip to acquire the angular velocity when the ankle’s dorsiflexion was performed. The output signals from both sensors were recorded and the processing with the algorithm was done offline. The second session was carried out with the ankle exoskeleton T-FLEX and a Serious Game, implementing the algorithm in real-time with a statistical feature selected from the first session as the threshold. The detection from the EMG algorithm was evaluated. The algorithm that T-FLEX already had for the movement intention detection with the IMU sensor also was evaluated. The results from the first session showed that the MEAN feature worked for the threshold establishment with the IMU sensor, and for the EMG sensor was the (VAR), presenting an error of less than 10% in the amount of False Positive (FP) and False Negative (FN) values. With this, the second session was carried out, showing that there was more precision handling the game using the IMU sensor than the EMG sensor. With the EMG sensor the maximum precision achieved was 89,7% and with the IMU sensor was 94.1%.86application/pdfhttps://doi.org/10.48713/10336_31583 https://repository.urosario.edu.co/handle/10336/31583engUniversidad del RosarioEscuela de Medicina y Ciencias de la SaludIngeniería BiomédicaAtribución-NoComercial-SinDerivadas 2.5 ColombiaAbierto (Texto Completo)EL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma.http://creativecommons.org/licenses/by-nc-nd/2.5/co/http://purl.org/coar/access_right/c_abf2W. Johnson, O. Onuma, M. Owolabi, and S. Sachdev,Stroke: a global response isneeded, 2016.H. Mamoru Yoshida, J. Barreira, and P. Teixeira Fernandes, “Motor skills, depressivesymptoms and cognitive functions in post-stroke patients,”Fisioter Pesqui, vol. 26,no. 1, pp. 9–14, 2019.R. Boian, J. E. Deutsch, and J. L. Grigore C. Burdea, “Post-Stroke Rehabilitation withthe Rutgers Ankle System: A Case Study,”PRESENCE, vol. 10, no. 4, pp. 416–430,2001.F. A. Silva, J. G. Zarruk, C. Quintero, W. Arenas, C. F. Rueda-Clausen, S. Y. Silva,and A. M. Estupiñán, “Cerebrovascular disease in Colombia,”Revista Colombiana deCardiología, vol. 13, no. 2, 2006.A. A. Duque and D. I. Lucumi,Caracterización del accidente cerebrovascular enColombia, 2019.C. Camejo, C. Legnani, A. Gaye, B. Arcieri, F. Brumett, L. Castro, A. Peña, F. Gómez,J. R. Higgie, F. Preve, and R. Salamano, “Stroke Unit at the Hospital de Clínicas:clinical-epidemiological behavior in patients with stroke (2007-2012),”Archivos deMedicina Interna, vol. 37, no. 1, 2015.L. W. Jian and S. N. W. Shamsuddin, “The design of virtual lower limb rehabilitation for post-stroke patients,”Indonesian Journal of Electrical Engineering and ComputerScience, vol. 16, no. 1, pp. 544–552, 2019.R. Jiménez-Fabián and O. Verlinden, “Review of control algorithms for robotic anklesystems in lower-limb orthoses, prostheses, and exoskeletons,”Medical Engineering &Physics, vol. 34, pp. 397–408, 2012.J. Toglia, G. Askin, L. M. Gerber, A. Jaywant, and M. W. O’Dell, “Participationin Younger and Older Adults Post-stroke: Frequency, Importance, and Desirability ofEngagement in Activities,”Frontiers in Neurology, 2019.V. Demarin, S. Morović, and R. BéNé, “Neuroplasticity,”Periodicum Biologorum,vol. 116, no. 2, pp. 209–211, 2014.A. V. Zakharov, V. A. Bulanov, E. V. Khivintseva, A. V. Kolsanov, Y. V. Bushkova,and G. E. Ivanova, “Stroke Affected Lower Limbs Rehabilitation Combining VirtualReality With Tactile Feedback,”Frontiers in Robotics and AI, 2020.54F. M. Lim, R. Foong, K. S. Goh, Q. L. Mok, B. H. J. Tan, S. L. Toh, and H. Yu,“Supine Gait Training Device for Stroke Rehabilitation – Design of a Compliant AnkleOrthosis,”IFMBE Proceedings, vol. 43, pp. 512–513, 2014.A. Esquenazi, S. Lee, A. Wikoff, A. Packel, T. Toczylowski, and J. Feeley, “A Compar-ison of Locomotor Therapy Interventions: Partial-Body WeightLSupported Treadmill,Lokomat, and G-EO Training in People With Traumatic Brain Injury,”AmericanAcademy of Physical Medicine and Rehabilitation, vol. 9, pp. 839–846,J. Fang, A. Vuckovic, S. Galen, C. Cossar, B. A. Conway, and K. J. Hunt, “Design andevaluation of a prototype gait orthosis for early rehabilitation of walking,”Technologyand Health Care, vol. 22, pp. 273–288, 2014.F. M. Alfieri, C. d. S. Dias, A. C. A. dos Santos, and L. R. Battistella, “Acute Effectof Robotic Therapy (G-EO System) on the Lower Limb Temperature Distribution of aPatient with Stroke Sequelae,”Case Reports in Neurological Medicine, vol. 1, pp. 2–5,2019.J. Stein, R. Hughes, S. E. Fasoli, H. I. Krebs, and N. Hogan, “Technological Aids for Motor Recovery,” in Stroke Recovery and Rehabilitation, 2020, pp. 307–321.Y. Lee, J. G. Her, Y. Choi, and H. Kim, “Effect of ankle-foot orthosis on lower limb muscle activities and static balance of stroke patients authors’ names,” Journal of Physical Therapy Science, vol. 26, no. 2, pp. 179–182, 2014, issn: 09155287. doi: 10.1589/jpts.26.179.M. Sartori, M. Reggiani, C. Mezzato, and E. Pagello, “A Lower Limb EMG-driven Biomechanical Model for Applications in Rehabilitation Robotics,” IEEE, pp. 1–7, 2009.J. C. Moreno, J. Figueiredo, and J. L. Pons, “Exoskeletons for lower-limb rehabilita tion,” in Rehabilitation Robotics. 2018, pp. 89–99.W. Alcocer, L. Vela, A. Blanco, J. Gonzalez, and M. Oliver, “Mayor Trends in the Development of Ankle Rehabilitation Devices,” DYNA, vol. 79, no. 176, 2012.Y. Ganesan, S. Gobee, and V. Durairajah, “Development of an Upper Limb Exoskele ton for Rehabilitation with Feedback from EMG and IMU Sensor,” Procedia Computer Science, vol. 76, no. 1, pp. 53–59, 2015.C. A. Aguilar Lazcano, O. O. Sandoval Gonzalez, J. A. Diaz Reyes, I. Herrera Aguilar, and A. Martínez Sibaja, Sistema de reconocimiento de gestos utilizando señales EMG para aplicaciones de control de prótesis biónicas y exoesqueletos de mano, 2016.K. Chen, B. Xiong, Y. Ren, A. Y. Dvorkin, D. Gaebler-Spira, C. E. Sisung, and L.-Q. Zhang, “Ankle Passive and Active Movement Training in Children with Acute Brain Injury Using a Wearable Robot,” J Rehabil Med, vol. 50, pp. 30–36, 2018.A. Jaume-i-Capó, J. V. Gomez, G. Moyà, and F. Perales, “Motivational Rehabilitation using Serious Games,” VAR., vol. 4, no. 9, pp. 167–173, 2013. 55A. Pino, D. Gomez-Vargas, M. Munera, and C. A. Cifuentes, “Visual Feedback Strategy based on Serious Games for Therapy with T-FLEX Ankle Exoskeleton,” The Interna tional Symposium on Wearable Robotics (WeRob2020) and WearRAcon Europe,, pp. 1– 2, 2020.N. R. García, “Diseño y Evaluación Ergonómica de Interfaces Físicas para la Órtesis Robótica de Tobillo (T-FLEX) a través de la Integración de Superficies Blandas,” 2019.D. Gomez-Vargas, F. Ballen-Moreno, P. Barria, R. Aguilar, J. M. Azorín, M. Munera, and C. A. Cifuentes, “Actuation system of the ankle exoskeleton T-FLEX: first use experimental validation in people with stroke,” Tech. Rep., pp. 1–17.D. Gomez-Vargas, M. J. Pinto-Bernal, F. Ballén-Moreno, M. Múnera, and C. A. Ci fuentes, “Therapy with T-FLEX Ankle-Exoskeleton for Motor Recovery:A Case Study with a Stroke Survivor,” in T-FLEX, 2020, pp. 1–6.M. Manchola, D. Serrano, D. Gómez, F. Ballen, D. Casas, M. Munera, and C. A. Cifuentes, “T-FLEX: Variable Stiffness Ankle-Foot Orthosis for Gait Assistance,” in Wearable Robotics: Challenges and Trends, 2018, pp. 160–164.C. L. Brockett and G. J. Chapman, “Biomechanics of the ankle,” Orthopaedics and Trauma, vol. 30, no. 3, pp. 232–238, 2016.J.-W. Lee, S.-W. Yoon, J.-H. Kim, Y.-P. Kim, and Y.-N. Kim, “The Effect of the Ankle Range of Motion on Balance Performance of Elderly People,” J.Phys. Ther. Sci, vol. 24, pp. 991–994, 2012.F. Gao, T. Tian, T. Yao, and Q. Zhang, “Human Gait Recognition Based on Mul tiple Feature Combination and Parameter Optimization Algorithms,” Computational Intelligence and Neuroscience, pp. 1–14, 2021.P. Aleixoa, J. V. Patto, A. Cardoso, H. Moreira, and J. Abrantes, “Ankle kinematics and kinetics during gait in healthy and rheumatoid arthritis post-menopausal women,” Somatosensory & Motor Research, vol. 36, no. 2, pp. 171–178, 2019.B. M. Logan, “Muscles,” in McMinn’s Color Atlas of Foot and Ankle Anatomy, 2012, pp. 116–121.S.-W. Yoon, J.-W. Lee, Y.-N. Kim, Y.-S. Kim, W.-S. Cho, and C.-B. Park, “Change in Ankle Dorsiflexion Range of Motion and Ultrasonographic Images of the Tibialis Anterior with Age,” J.Phys. Ther. Sci, vol. 23, pp. 813–815, 2011.S. Dorsch, L. Ada, C. G. Canning, M. Al-Zharani, and C. Dean, “The Strength of the Ankle Dorsiflexors Has a Significant Contribution to Walking Speed in People Who Can Walk Independently After Stroke: An Observational Study,” Arch Phys Med Rehabil, vol. 93, pp. 1072–2076, 2012.B. F. Koseoglu, A. Dogan, H. U. Tatli, D. S. Ozcan, and C. S. Polat, “Can kinesio tape be used as an ankle training method in the rehabilitation of the stroke patients?” Complementary Therapies in Clinical Practice, vol. 27, pp. 46–51, 2017. 56D. H. Richie, “Functional Instability of the Ankle and the Role of Neuromuscular Control: A Comprehensive Review,” The Journal of Foot and Ankle Surgery, vol. 40, no. 4, pp. 240–251, 2001.P. S. Karakkattil, E. Trudelle-Jackson, A. Medley, and C. Swank, “Effects of two different types of ankle–foot orthoses on gait outcomes in patients with subacute stroke: a randomized crossover trial.,” Clinical Rehabilitation, vol. 34, no. 8, pp. 1094–1102, 2020.L. W. Forrester, A. Roy, R. N. Goodman, J. Rietschel, J. E. Barton, H. I. Krebs, and R. F. Macko, “Clinical application of a modular ankle robot for stroke rehabilitation,” NeuroRehabilitation, vol. 33, pp. 57–97, 2013.L. R. Sheffler and J. Chae, “Technological Advances in Interventions to Enhance Post stroke Gait,” Phys Med Rehabil Clin N Am, vol. 24, pp. 305–323, 2013.E. Zimmerman, G. Carnaby, C. L. Lazarus, and G. A. Malandraki, “Motor Learn ing, Neuroplasticity, and Strength and Skill Training: Moving From Compensation to Retraining in Behavioral Management of Dysphagia,” American Journal of Speech Language Pathology, vol. 29, pp. 1065–1077, 2020.L. Chen, S. Xiong, Y. Liu, M. Lin, L. Zhu, R. Zhong, J. Zhao, W. Liu, J. Wang, and X. Shang, “Comparison of Motor Relearning Program versus Bobath Approach for Prevention of Poststroke Apathy: A Randomized Controlled Trial,” Journal of Stroke and Cerebrovascular Diseases, vol. 28, no. 3, pp. 655–664, 2019.P. Péran, F. Nemmi, C. Dutilleul, L. Finamore, C. F. Caravasso, E. Troisi, M. Iosa, U. Sabatini, and M. G. Grasso, “Neuroplasticity and brain reorganization associated with positive outcomes of multidisciplinary rehabilitation in progressive multiple sclerosis: A fMRI study,” Multiple Sclerosis and Related Disorders, vol. 42, pp. 1–7, 2020.J. Jiang, K.-M. Lee, and J. Ji, “Review of anatomy-based ankle–foot robotics for mind, motor and motion recovery following stroke: design considerations and needs,” International Journal of Intelligent Robotics and Applications, pp. 1–16, 2018.D. Park, H.-S. Cynn, C. Yi, W. J. Choi, J. Shim, and D.-W. Oh, “Four-week training involving self-ankle mobilization with movement versus calf muscle stretching in patients with chronic stroke: a randomized controlled study,” Topics in Stroke Rehabilitation, pp. 1–9, 2019.L.-F. Yeung, C. C. Y. Lau, C. W. K. Lai, Y. O. Y. Soo, M.-L. Chan, and R. K. Y. Tong, “Effects of wearable ankle robotics for stair and over-ground training on sub acute stroke: a randomized controlled trial,” J NeuroEngineering Rehabil, vol. 18, no. 19, pp. 1–10, 2021.G. Chen, P. Qi, Z. Guo, and H. Yu, “Mechanical design and evaluation of a com pact portable knee–ankle–foot robot for gait rehabilitation,” Mechanism and Machine Theory, vol. 103, pp. 51–64, 2016.Q. Liu, C. Wang, J. J. Long, T. Sun, L. Duan, X. Zhang, B. Zhang, Y. Shen, W. Shang, Z. Lin, Y. Wang, J. Xia, J. Wei, W. Li, and Z. Wu, “Development of a New Robotic Ankle Rehabilitation Platform for Hemiplegic Patients after Stroke,” Journal of Healthcare Engineering, pp. 1–12, 2018. 57R. N. Goodman, J. C. Rietschel, A. Roy, B. C. Jung, J. Diaz, R. F. Macko, and L. W. Forrester, “Increased reward in ankle robotics training enhances motor control and cortical efficiency in stroke,” JRRD, vol. 51, no. 2, pp. 213–227, 2014.S. M. S. Hasan, M. R. Siddiquee, R. Atri, R. Ramon, J. S. Marquez, and O. Bai, “Prediction of gait intention from pre-movement EEG signals: a feasibility study,” Journal of NeuroEngineering and Rehabilitation, vol. 17, no. 50, pp. 1–16, 2020.J. Lobo-Prat, P. N. Kooren, A. H. Stienen, J. L. Herder, B. F. Koopman, and P. H. Veltink, “Non-invasive control interfaces for intention detection in active movement assistive devices,” Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 168, pp. 1–22, 2014.J. de Vries, A. van Ommeren, G. Prange-Lasonder, J. Rietman, and P. Veltink, “De tection of the intention to grasp during reach movements,” Journal of Rehabilitation and Assistive Technologies Engineering, vol. 5, pp. 1–9, 2017.E. Wentink, S. Beijen, H. Hermens, J. Rietman, and P. Veltink, “Intention detection of gait initiation using EMG and kinematic data,” Gait & Posture, vol. 37, pp. 223–228, 2013.F. Feuvrier, B. Sijobert, C. Azevedo, K. Griffiths, S. Alonso, A. Dupeyron, I. Laffont, and J. Froger, “Inertial measurement unit compared to an optical motion capturing system in post-stroke individuals with foot-drop syndrome,” Annals of Physical and Rehabilitation Medicine, vol. 63, pp. 195–201, 2020.I.-S. Weon and S.-G. Lee, “Intelligent robotic walker with actively controlled human interaction,” Wiley Etri Journal, vol. 40, no. 4, pp. 522–530, 2018.D. Novak, P. Rebersek, S. Marco, M. D. Rossi, M. Donati, J. Podobnik, T. Beravs, T. Lenzi, N. Vitiello, M. C. Carrozza, and M. Munih, “Automated detection of gait initiation and termination using wearable sensors,” Medical Engineering & Physics, vol. 35, pp. 1713–1720, 2013.V. Hernandez, D. Dadkhah, V. Babakeshizadeh, and D. Kulic, “Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach,” Gait & Posture, vol. 83, pp. 185–193, 2021.E. Wentink, V. Schut, E. Prinsen, J. Rietman, and P. Veltink, “Detection of the onset of gait initiation using kinematic sensors and EMG in transfemoral amputees,” Gait & Posture, vol. 39, pp. 391–396, 2014.S. A. Khomami and S. Shamekhi, “Persian sign language recognition using IMU and surface EMG sensors,” Measurement, vol. 168, pp. 1–9, 2021.D. Buongiorno, M. Barsotti, F. Barone, V. Bevilacqua, and A. Frisoli, “A Linear Ap proach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Inten tion Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints,” Front. Neurorobot, vol. 12, pp. 1–12, 2018. 58W. Hassani, S. Mohammed, H. Rifaï, and Y. Amirat, “Powered orthosis for lower limb movements assistance and rehabilitation,” Control Engineering Practice, vol. 26, pp. 245–253, 2014.A. Avila and J.-Y. Chang, “EMG onset detection and upper limb movements identifi cation algorithm,” Microsyst Technol, vol. 20, pp. 1635–1640, 2014.D.-H. Moon, D. Kim, and Y.-D. Hong, “Intention Detection Using Physical Sensors and Electromyogram for a Single Leg Knee Exoskeleton,” Sensors, vol. 19, no. 4447, pp. 1–15, 2019.Y. Yin, “Estimation of Skeletal Muscle Activation and Contraction Force Based on EMG Signals,” in Biomechanical Principles on Force Generation and Control of Skele tal Muscle and their Applications in Robotic Exoskeleton, 2020, pp. 91–114.C. A. Quinayás-Burgos and C. A. Gaviria-López, “Movement intention detection sys tem for myoelectric control of a prosthetic robotic hand,” Ing. Univ, vol. 19, no. 1, pp. 27–50, 2015.F. Astudillo, J. Charry, I. Minchala, and S. Wong, “Lower limbs motion intention detection by using pattern recognition,” in Lower limbs motion intention detection by using pattern recognition, 2019, pp. 1–6.T. Cao, D. Liu, Q. Wang, O. Bai, and J. Sun, “Surface Electromyography-Based Action Recognition and Manipulator Control,” applied sciences, vol. 10, no. 5823, pp. 1–20, 2020.G. J. Androwis, R. Pilkar, A. Ramanujam, and K. J. Nolan, “Electromyography As sessment During Gait in a Robotic Exoskeleton for Acute Stroke,” Front. Neurol., vol. 9, no. 630, 2018.S. Y. Gordleeva, S. A. Lobov, N. A. Grigorev, A. O. Savosenkov, M. O. Shamshin, M. V. Lukoyanov, M. A. Khoruzhko, and V. B. Kazantsev, “Real-Time EEG–EMG Hu man–Machine Interface-Based Control System for a Lower-Limb Exoskeleton,” IEEE ACCESS, vol. 8, pp. 84 070–84 081, 2020.S. Peters, T. Ivanova, B. Lakhani, L. A. Boyd, and S. J. Garland, “Neuroplasticity of Cortical Planning for Initiating Stepping Poststroke: A Case Series,” Journal of Neurologic Physical Therapy, vol. 44, no. 2, pp. 164–172, 2020.M. Li, G. Xu, J. Xie, and C. Chen, “A review: Motor rehabilitation after stroke with control based on human intent,” Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine, pp. 1–62, 2018.N. Irastorza-Landa, A. Sarasola-Sanz, E. López-Larraz, C. Bibián, F. Shiman, N. Bir baumer, and A. Ramos-Murguialday, “Design of Continuous EMG Classification ap proaches towards the Control of a Robotic Exoskeleton in Reaching Movements,” IEEE International Conference on Rehabilitation Robotics, 2017.M. Ozsert, O. Yavuz, and L. Durak-Ata, “Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms,” Com puter Methods in Biomechanics and Biomedical Engineering, vol. 14, no. 6, pp. 521– 525, 2011. 59C. Liu, L. Pan, Z. Gu, J. Wang, Y. Ren, and Z. Wang, “Valid Probabilistic Anomaly Detection Models for System Logs,” Wireless Communications and Mobile Computing, pp. 1–12, 2020.J. Mickelborough, M. L. van der Linden, R. C. Tallis, and A. R. Ennos, “Muscle activity during gait initiation in normal elderly people,” Gait & Posture, vol. 19, no. 1, pp. 50– 57, 2004.L. Hardesty, Explained: Neural networks, 2017.C. Lersviriyanantakul, A. Booranawong, K. Sengchuai, P. Phukpattaranont, B. Wongkittisuksa, and N. Jindapetch, “Implementation of a Real-Time Automatic Onset Time Detection for Surface Electromyography Measurement Systems Using NI myRio,” Thermal Science, vol. 20, no. 2, S591–S602, 2016.M. Tabie and E. A. Kirchner, EMG Onset Detection Comparison of different methods for a movement prediction task based on EMG, 2013.A. Avila and J.-Y. Chang, “EMG onset detection and upper limb movements identifi cation algorithm,” Microsyst Technol, vol. 20, pp. 1635–1640, 2014.A. Kontunen, V. Rantanen, A. Vehkaoja, M. Ilves, J. Lylykangas, E. Makela, M. Rautiainen, V. Surakka, and J. Lekkala, “Low-latency EMG Onset and Termination Detection for Facial Pacing,” in IFMBE proceedings, 2018, pp. 1016–1019.J.-H. Park, H.-S. Moon, H. Kim, and S.-T. Chung, “Detection of Movement Intention for Operating Methods of Serious Games,” Appl. Sci., vol. 11, no. 883, pp. 1–14, 2021.S.-N. Jeon and J.-H. Choi, “The effects of ankle joint strategy exercises with and without visual feedback on the dynamic balance of stroke patients,” J Phys Ther Sci, vol. 27, no. 8, 2015.C. Prahm, I. Vujaklija, F. Kayali, P. Purgathofer, and O. C. Aszmann, “Game-Based Rehabilitation for Myoelectric Prosthesis Control,” Jmir Serious Games, vol. 5, no. 1, pp. 1–13, 2017.R. de la Rosa, A. Alonso, S. de la Rosa, and D. Abásolo, “Myo-Pong: a neuromuscular game for the UVa-Neuromuscular Training System Platform,” IEEE, p. 61, 2008.Á. Gutiérrez, D. Sepúlveda-Muñoz, Á. Gil-Agudo, and A. d. l. R. Guzmán, “Serious Game Platform with Haptic Feedback and EMG Monitoring for Upper Limb Reha bilitation and Smoothness Quantification on Spinal Cord Injury Patients,” Appl. Sci., vol. 10, no. 963, pp. 1–17, 2020.L. van Dijk, C. K. van der Sluis, H. W. van Dijk, and R. M. Bongers, “Learning an EMG Controlled Game: TaskSpecific Adaptations and Transfer,” Plos One, vol. 11, no. 8, pp. 1–14, 2016.R. Labruyére, C. N. Gerber, K. Birrer-Brutsch, A. Meyer-Heim, and H. J. van Hedel, “Requirements for and impact of a serious game for neuro-pediatric robot-assisted gait training,” Hubertus J.A. van Hedel, vol. 34, pp. 3906–3915, 2013.J. C. Pérez-Ibarra and A. A. G. Siqueira, “Comparison of Kinematic and EMG parame ters between unassisted, fixed- and adaptive-stiffness robotic-assisted ankle movements in post-stroke subjects,” IEEE, pp. 461–466, 2017. 60G. Serrano, Electromiografia, 2020.A. P. Laquidara, L. M. Zerbino, C. Lagraña, and E. Yedinak, “Automioestimulador: Biofeedback Aplicado a la Rehabilitación,” Segundas Jornadas de Investigación y Transferencia, pp. 293–298, 2013.Y. Lan, J. Yao, and J. Dewald P.A., “The Impact of Shoulder Abduction Loading on EMG-based Intention Detection of Hand Opening and Closing After Stroke,” Conf Proc IEEE Eng Med Biol Soc., p. 1, 2011.A. Technologies, MyoWare™ Muscle Sensor (AT-04-001), 2015.B. Sensortec, BNO005 Intelligent 9-axis absolute orientation sensor, 2020.B. E. . Kaminski, Emg Circuit, 2015.D. A. G. Vargas, “Development of control strategies for a variable stiffness ankle exoskeleton for gait rehabilitation,” Ph.D. dissertation, 2020, pp. 1–136.A. D. P. 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