Identification of movement intention of gait on various terrains -a bioinspired approach-

En este documento se propone una aproximación al entendimiento del lenguaje neuromotor de una persona con una amputación por encima de rodilla, mediante la identificación de su intención de movimiento a partir de la percepción de las manifestaciones tanto internas como externas de los Patrones de Ac...

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Autores:: Caicedo, Eduardo F.
Campo Salazar, Oscar Iván

Tipo de recurso:: Article of journal

Fecha de publicación:: 2017

Institución:: Universidad Autónoma de Occidente

Repositorio:: RED: Repositorio Educativo Digital UAO

Idioma:: eng

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dc.title.eng.fl_str_mv	Identification of movement intention of gait on various terrains -a bioinspired approach-
dc.title.alternative.spa.fl_str_mv	Identificación de intención de movimiento de marcha sobre diferentes terrenos -una aproximación bioinspirada-
title	Identification of movement intention of gait on various terrains -a bioinspired approach-
spellingShingle	Identification of movement intention of gait on various terrains -a bioinspired approach- Extremidades artificiales Artificial limbs Prótesis Prosthesis Algoritmo de identificación Intención del usuario Patrón de acción fija artificial Identification algorithm Artificial fixed action pattern User’s intention
title_short	Identification of movement intention of gait on various terrains -a bioinspired approach-
title_full	Identification of movement intention of gait on various terrains -a bioinspired approach-
title_fullStr	Identification of movement intention of gait on various terrains -a bioinspired approach-
title_full_unstemmed	Identification of movement intention of gait on various terrains -a bioinspired approach-
title_sort	Identification of movement intention of gait on various terrains -a bioinspired approach-
dc.creator.fl_str_mv	Caicedo, Eduardo F. Campo Salazar, Oscar Iván
dc.contributor.author.none.fl_str_mv	Caicedo, Eduardo F. Campo Salazar, Oscar Iván
dc.subject.lemb.spa.fl_str_mv	Extremidades artificiales
topic	Extremidades artificiales Artificial limbs Prótesis Prosthesis Algoritmo de identificación Intención del usuario Patrón de acción fija artificial Identification algorithm Artificial fixed action pattern User’s intention
dc.subject.lemb.eng.fl_str_mv	Artificial limbs
dc.subject.armarc.spa.fl_str_mv	Prótesis
dc.subject.armarc.eng.fl_str_mv	Prosthesis
dc.subject.proposal.spa.fl_str_mv	Algoritmo de identificación Intención del usuario Patrón de acción fija artificial
dc.subject.proposal.eng.fl_str_mv	Identification algorithm Artificial fixed action pattern User’s intention
description	En este documento se propone una aproximación al entendimiento del lenguaje neuromotor de una persona con una amputación por encima de rodilla, mediante la identificación de su intención de movimiento a partir de la percepción de las manifestaciones tanto internas como externas de los Patrones de Acción Fija (PAF) mediante el uso de una propiocepción artificial y la exterocepción de su prótesis durante el desarrollo de diferentes gestos (marcha, subir y bajar escaleras y subir y bajar rampas). Se presentan la formalización de una expresión General del Gesto Rítmico, los procedimientos para la generación de PAF artificiales y un Algoritmo de Respuesta ante el Desarrollo de Gestos. Mediante la identificación de la intención del usuario a través de la información propioceptiva y exteroceptiva, la prótesis discrimina entre un repertorio de PAF artificiales y selecciona el más adecuado para satisfacer las necesidades de movimiento del usuario. Los datos experimentales de las pruebas desarrolladas en individuos sanos y amputados mostraron un alto desempeño en la identificación de la intención del usuario (97.06% de identificaciones correctas) y un buen seguimiento de los gestos de movimiento independientemente de la velocidad con que fueron ejecutados
publishDate	2017
dc.date.issued.none.fl_str_mv	2017
dc.date.accessioned.none.fl_str_mv	2019-09-18T19:13:41Z
dc.date.available.none.fl_str_mv	2019-09-18T19:13:41Z
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dc.relation.cites.none.fl_str_mv	Campo, O & Caicedo, E. F., (2017). Identification of movement intention of gait on various terrains -a bioinspired approach-. Ingeniería y competitividad, 19(2), 69-82. https://dx.doi.org/10.25100/iyc.v19i2.5294
dc.relation.ispartofjournal.spa.fl_str_mv	Ingeniería y competitividad
dc.relation.references.none.fl_str_mv	James W. Principles of psychology. New York: Dover; 1950 (1890) Brown G. On the activities of the central nervous system of the un-born fœtus of the cat; with a discussion of the question whether progression (walking, etc.) is a "learnt " complex. Journal of Physiology. 1915;49(4):208-215 Pearson K, Gordon J. Spinal reflexes. In: Kandel ER, Schwartz JH, Jessell TM. Principles of neural science. New York, NY: McGraw-Hill; 2000. p. 713-736 Brown G. On the nature of the fundamental activity of nervous centers; together with an analysis of the conditioning of rhytmic activity in progression and a theory of the evolution of the nervous system. Journal of Physiology. 1914;48(1):18-46 Buzsáki G, Peyrache A, Kubie J. Emergence of cognition from action. Cold Spring Harbor Symposia on Quantitative Biology. 2015;79:41-50 Alnajjar F, Itkonen M, Berenz V, Tournier M, Nagai C, Shimoda S. Sensory synergy as environmental input integration. Frontiers in Neuroscience. 2015;8:436 Llinas R, Roy S. The ' prediction imperative ' as the basis for self-awareness. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 2009;364 (1521):1301-1307 Buzsáki G. Neural syntax: cell assemblies, synapsembles, and readers. Neuron. 2010;68(3):362-385 Watson BO, Buzsáki G. Sleep, Memory & Brain Rhythms. Journal of Neurophysiology. 2015;144(1):67-82 Beer R, Chiel H, Sterling L. A biological perspective on autonomous agent design. Robotics and Autonomous Systems. 1990;6(1-2):169-186 Llinas R. I of the vortex: From neurons to self. Cambridge: MIT press; 2002. 302 p. Guertin PA. Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations. Frontiers in Neuroscience. 2013;8(3):183 Aoi S, Ogihara N, Funato T, Sugimoto Y, Tsuchiya K. Evaluating functional roles of phase resetting in generation of adaptive human bipedal walking with a physiologically based model of the spinal pattern generator. Biological Cybernetics. 2010;102(5):373-387 Nandi GC, Gupta B. Bio-inspired control methodology of walking for intelligent prosthetic knee. In: Proceedings of the 3rd International Conference of Informatics in Control, Automation and Robotics, ICINCO. Nice, France. IEEE; 2006. p. 2368-2373 Bauer C, Braun S, Chen Y, Jakob W, Mikut R. Optimization of artificial central pattern generators with evolutionary algorithms. In: Mikut R, ed. Proceedings of the 18 Workshop Computational Intelligence; Dortmund, Germany: Universit tsverlag Karlsruhe; 2006. p. 40-54 Ambroise M , Levi T , Joucla S , Yvert B , Saïghi S . Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments. Frontiers in Neuroscience. 2013;7:215 Alcock J. Evolution, Nervous Systems, and Behavior. In: Alcock J. Animal Behavior: An Evolutionary Approach (10th edition). Sunderland, Massachusetts: Sinauer Associates, Inc.; 1998. p. 363-9 Mazur JE. Learning and Behavior (6th Edition). New Jersey: Prentice Hall; 2005. 448 p. Campbell NA. Animal behavior. In: Biology (9 ed). New York: Benjamin Cummings; 1996. p. 11-19 Wu G, Sieglerb S, Allardc P, Kirtleyd C, Leardinie A, Rosenbaumf D, et al. ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion-part I: ankle, hip, and spine. Journal of Biomechanics. 2002;35(4):543-8 Ferrari A, Benedetti MG, Pavan E, Frigo C, Bettinelli D, Rabuffetti M, et al. Quantitative comparison of five current protocols in gait analysis. Gait Posture. 2008;28(2):207-16 Silva J, Chau T, Naumann S, Heim W. Systematic characterisation of silicon-embedded accelerometers for mechanomyography. Medical and Biological Engineering and Computing. 2003;41(3):290-5 Silva J. Mechanomyography sensor design and multisensor fusion for upper-limb prosthesis control. Master thesis. Toronto: Mechanical Engineering Department, University of Toronto; 2004 Madeleine P, Cescon C, Farina D. Spatial and force dependency of mechanomyographic signal features. Journal of Neuroscience Methods. 2006;158(1):89-99 Scott RN. An introduction to myoelectric prostheses,Volumen 1 U.N.B. Monographs on Myoelectric Prostheses. Bio-Engineering Institute, University of New Brunswick, 1984; pp 17 Wilkenfeld AJ. Biologically inspired autoadaptive control of a knee prosthesis. Master thesis. Massachusetts, USA: Massachusetts Institute of Technology; 2000 Jin D, Yang J, Zhang R, Wang R, Zhang J. Terrain identification for prosthetic knees based on electromyographic signal features. Tsinghua Science and Technology. 2006 ;11(1):74-9 Chen B, Zheng E, Fan X, Liang T, Wang Q, Wei K, et al. Locomotion mode classification using a wearable capacitive sensing system. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013;21(5):744-55 Wang F, Su J, Xie H, Xu X. Terrain identification of intelligent bionic leg based on ground reaction force. In: IEEE International Conference on Integration Technology, ICIT-07; Shenzhen, China: IEEE; 2007. p. 609-13 Yuan K, Sun S, Wang Z, Wang Q, Wang L. A fuzzy logic based terrain identification approach to prosthesis control using multi-sensor fusion. In: IEEE International Conference on Robotics and Automation (ICRA); Karlsruhe, Alemania: IEEE; 2013. p. 3376-81 Smith L, Hargrove L, Lock B, Kuiken T. Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2011;19(2):186-92
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spelling	Caicedo, Eduardo F.2ff60663eba7dde151761bb947f5b8f7Campo Salazar, Oscar Ivánvirtual::148-1Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-09-18T19:13:41Z2019-09-18T19:13:41Z20172027-8284 (en línea)0123-3033 (impresa)http://hdl.handle.net/10614/11122https://dx.doi.org/10.25100/iyc.v19i2.5294En este documento se propone una aproximación al entendimiento del lenguaje neuromotor de una persona con una amputación por encima de rodilla, mediante la identificación de su intención de movimiento a partir de la percepción de las manifestaciones tanto internas como externas de los Patrones de Acción Fija (PAF) mediante el uso de una propiocepción artificial y la exterocepción de su prótesis durante el desarrollo de diferentes gestos (marcha, subir y bajar escaleras y subir y bajar rampas). Se presentan la formalización de una expresión General del Gesto Rítmico, los procedimientos para la generación de PAF artificiales y un Algoritmo de Respuesta ante el Desarrollo de Gestos. Mediante la identificación de la intención del usuario a través de la información propioceptiva y exteroceptiva, la prótesis discrimina entre un repertorio de PAF artificiales y selecciona el más adecuado para satisfacer las necesidades de movimiento del usuario. Los datos experimentales de las pruebas desarrolladas en individuos sanos y amputados mostraron un alto desempeño en la identificación de la intención del usuario (97.06% de identificaciones correctas) y un buen seguimiento de los gestos de movimiento independientemente de la velocidad con que fueron ejecutadosIn this paper we propose an approach to the neuromotor language of the transfemoral amputee user. We do this by identifying the user’s intention from the perception of both internal and external manifestations (to be explained into a next section) of Fixed Action Patterns (FAPs, by making use of artificial proprioception and exteroception of the prosthesis. The formalization of a General Expression of the Rhythmic Gesture, generation procedures for artificial FAPs, and a Response Algorithm for Gestures Development are presented. By identifying the user’s intention through proprioceptive and exteroceptive information, the prosthesis discriminates between repertories of artificial FAPs and chooses the most suitable one to meet the users’s requirements. Experimental data of tests carried out in healthy and amputee individuals showed high performance on identification (97.06 % of true identifications) of the user’s intention and good tracking of gestures such as gait, walking up stairs, down stairs, up hill and down hill, independently of the speed of execution of the gestureapplication/pdf14 páginasengUniversidad del Valle. Facultad de IngenieríaDerechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Identification of movement intention of gait on various terrains -a bioinspired approach-Identificación de intención de movimiento de marcha sobre diferentes terrenos -una aproximación bioinspirada-Artículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Extremidades artificialesArtificial limbsPrótesisProsthesisAlgoritmo de identificaciónIntención del usuarioPatrón de acción fija artificialIdentification algorithmArtificial fixed action patternUser’s intention8226919Campo, O & Caicedo, E. F., (2017). Identification of movement intention of gait on various terrains -a bioinspired approach-. Ingeniería y competitividad, 19(2), 69-82. https://dx.doi.org/10.25100/iyc.v19i2.5294Ingeniería y competitividadJames W. Principles of psychology. New York: Dover; 1950 (1890)Brown G. On the activities of the central nervous system of the un-born fœtus of the cat; with a discussion of the question whether progression (walking, etc.) is a "learnt " complex. Journal of Physiology. 1915;49(4):208-215Pearson K, Gordon J. Spinal reflexes. In: Kandel ER, Schwartz JH, Jessell TM. Principles of neural science. New York, NY: McGraw-Hill; 2000. p. 713-736Brown G. On the nature of the fundamental activity of nervous centers; together with an analysis of the conditioning of rhytmic activity in progression and a theory of the evolution of the nervous system. Journal of Physiology. 1914;48(1):18-46Buzsáki G, Peyrache A, Kubie J. Emergence of cognition from action. Cold Spring Harbor Symposia on Quantitative Biology. 2015;79:41-50Alnajjar F, Itkonen M, Berenz V, Tournier M, Nagai C, Shimoda S. Sensory synergy as environmental input integration. Frontiers in Neuroscience. 2015;8:436Llinas R, Roy S. The ' prediction imperative ' as the basis for self-awareness. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 2009;364 (1521):1301-1307Buzsáki G. Neural syntax: cell assemblies, synapsembles, and readers. Neuron. 2010;68(3):362-385Watson BO, Buzsáki G. Sleep, Memory & Brain Rhythms. Journal of Neurophysiology. 2015;144(1):67-82Beer R, Chiel H, Sterling L. A biological perspective on autonomous agent design. Robotics and Autonomous Systems. 1990;6(1-2):169-186Llinas R. I of the vortex: From neurons to self. Cambridge: MIT press; 2002. 302 p.Guertin PA. Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations. Frontiers in Neuroscience. 2013;8(3):183Aoi S, Ogihara N, Funato T, Sugimoto Y, Tsuchiya K. Evaluating functional roles of phase resetting in generation of adaptive human bipedal walking with a physiologically based model of the spinal pattern generator. Biological Cybernetics. 2010;102(5):373-387Nandi GC, Gupta B. Bio-inspired control methodology of walking for intelligent prosthetic knee. In: Proceedings of the 3rd International Conference of Informatics in Control, Automation and Robotics, ICINCO. Nice, France. IEEE; 2006. p. 2368-2373Bauer C, Braun S, Chen Y, Jakob W, Mikut R. Optimization of artificial central pattern generators with evolutionary algorithms. In: Mikut R, ed. Proceedings of the 18 Workshop Computational Intelligence; Dortmund, Germany: Universit tsverlag Karlsruhe; 2006. p. 40-54Ambroise M , Levi T , Joucla S , Yvert B , Saïghi S . Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments. Frontiers in Neuroscience. 2013;7:215Alcock J. Evolution, Nervous Systems, and Behavior. In: Alcock J. Animal Behavior: An Evolutionary Approach (10th edition). Sunderland, Massachusetts: Sinauer Associates, Inc.; 1998. p. 363-9Mazur JE. Learning and Behavior (6th Edition). New Jersey: Prentice Hall; 2005. 448 p.Campbell NA. Animal behavior. In: Biology (9 ed). New York: Benjamin Cummings; 1996. p. 11-19Wu G, Sieglerb S, Allardc P, Kirtleyd C, Leardinie A, Rosenbaumf D, et al. ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion-part I: ankle, hip, and spine. Journal of Biomechanics. 2002;35(4):543-8Ferrari A, Benedetti MG, Pavan E, Frigo C, Bettinelli D, Rabuffetti M, et al. Quantitative comparison of five current protocols in gait analysis. Gait Posture. 2008;28(2):207-16Silva J, Chau T, Naumann S, Heim W. Systematic characterisation of silicon-embedded accelerometers for mechanomyography. Medical and Biological Engineering and Computing. 2003;41(3):290-5Silva J. Mechanomyography sensor design and multisensor fusion for upper-limb prosthesis control. Master thesis. Toronto: Mechanical Engineering Department, University of Toronto; 2004Madeleine P, Cescon C, Farina D. Spatial and force dependency of mechanomyographic signal features. Journal of Neuroscience Methods. 2006;158(1):89-99Scott RN. An introduction to myoelectric prostheses,Volumen 1 U.N.B. Monographs on Myoelectric Prostheses. Bio-Engineering Institute, University of New Brunswick, 1984; pp 17Wilkenfeld AJ. Biologically inspired autoadaptive control of a knee prosthesis. Master thesis. Massachusetts, USA: Massachusetts Institute of Technology; 2000Jin D, Yang J, Zhang R, Wang R, Zhang J. Terrain identification for prosthetic knees based on electromyographic signal features. Tsinghua Science and Technology. 2006 ;11(1):74-9Chen B, Zheng E, Fan X, Liang T, Wang Q, Wei K, et al. Locomotion mode classification using a wearable capacitive sensing system. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013;21(5):744-55Wang F, Su J, Xie H, Xu X. Terrain identification of intelligent bionic leg based on ground reaction force. In: IEEE International Conference on Integration Technology, ICIT-07; Shenzhen, China: IEEE; 2007. p. 609-13Yuan K, Sun S, Wang Z, Wang Q, Wang L. A fuzzy logic based terrain identification approach to prosthesis control using multi-sensor fusion. In: IEEE International Conference on Robotics and Automation (ICRA); Karlsruhe, Alemania: IEEE; 2013. p. 3376-81Smith L, Hargrove L, Lock B, Kuiken T. Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2011;19(2):186-92Publicationa358342d-0532-401b-97fa-4986de22c9cdvirtual::148-1a358342d-0532-401b-97fa-4986de22c9cdvirtual::148-1https://orcid.org/0000-0002-5007-9613virtual::148-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000142433virtual::148-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/b30131ed-5735-4a89-a85e-b36d5eece3f1/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/f3e996f2-54bb-4d8c-a770-225bd964ded2/download20b5ba22b1117f71589c7318baa2c560MD53ORIGINAL00335_Identification of movement intention of gait on various terrains a bioinspired approach.pdf00335_Identification of movement intention of gait on various terrains a bioinspired approach.pdfArchivo texto completo del artículoapplication/pdf967380https://red.uao.edu.co/bitstreams/67b20a59-1478-497c-aa88-f1cfd0209a8a/download0c8de1e3ecdd3c00fb3e4bdf24182b7eMD54TEXT00335_Identification of movement intention of gait on various terrains a bioinspired approach.pdf.txt00335_Identification of movement intention of gait on various terrains a bioinspired approach.pdf.txtExtracted texttext/plain44063https://red.uao.edu.co/bitstreams/fd279c2a-bf27-4586-b80b-b36d4e0e913f/downloadb71ab9e8b79569fc7ea3878a6ad7404bMD55THUMBNAIL00335_Identification of movement intention of gait on various terrains a bioinspired approach.pdf.jpg00335_Identification of movement intention of gait on various terrains a bioinspired approach.pdf.jpgGenerated Thumbnailimage/jpeg14087https://red.uao.edu.co/bitstreams/00362ee1-c982-4fe8-bf31-96ceded58f5f/download7f56f3c5fb4a5b4115351df718cbba0eMD5610614/11122oai:red.uao.edu.co:10614/111222024-02-01 08:28:04.029https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidenteopen.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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

Identification of movement intention of gait on various terrains -a bioinspired approach-

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