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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.authorMayorca-Torres D.
dc.contributor.authorCaicedo-Eraso J.C.
dc.contributor.authorPeluffo-Ordóñez D.H.
dc.date.accessioned2024-12-02T20:15:58Z
dc.date.available2024-12-02T20:15:58Z
dc.date.issued2020
dc.identifier.issn20885334
dc.identifier.urihttps://hdl.handle.net/20.500.14112/28997
dc.description.abstractInside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia, however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the kneejoint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient's knee-joint kinematics during a human gait. © 2020 Insight Society.
dc.description.sponsorshipThis research is supported by the seed group 'SIngBio Seedbed of Research in Engineering and Biomedical Sciences' of the Universidad de Caldas. In the same way, this work was supported by the Mechatronic Engineering research Group of the Universidad Mariana. Also, the authors are very grateful for the valuable support given by SDAS Research Group (www.sdas-group.com), especially for its Junior Researcher, Jaime A. Riascos, who supported us in the revision and writing process of the manuscript.
dc.format7
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInsight Society
dc.rights.uriAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.sourceInternational Journal on Advanced Science, Engineering and Information Technology
dc.sourceInt. J. Adv. Sci. Eng. Inf. Technol.
dc.sourceScopus
dc.titleKnee joint angle measuring portable embedded system based on inertial measurement units for gait analysis
datacite.contributorFacultad de Ingeniería, Universidad Mariana, Pasto (Nariño), 520001, Colombia
datacite.contributorFacultad de Ingeniería, Universidad de Caldas, Manizales (Caldas), 170001, Colombia
datacite.contributorEscuela de Ciencias Matemáticas y Computacionales, Yachay Tech, San Miguel (Urcuquí), 100650, Ecuador
datacite.contributorCorporación Universitaria Autónoma de Nariño, Pasto, 520001, Colombia
datacite.contributorMayorca-Torres D., Facultad de Ingeniería, Universidad Mariana, Pasto (Nariño), 520001, Colombia, Facultad de Ingeniería, Universidad de Caldas, Manizales (Caldas), 170001, Colombia
datacite.contributorCaicedo-Eraso J.C., Facultad de Ingeniería, Universidad de Caldas, Manizales (Caldas), 170001, Colombia
datacite.contributorPeluffo-Ordóñez D.H., Escuela de Ciencias Matemáticas y Computacionales, Yachay Tech, San Miguel (Urcuquí), 100650, Ecuador, Corporación Universitaria Autónoma de Nariño, Pasto, 520001, Colombia
datacite.rightshttp://purl.org/coar/access_right/c_abf2
oaire.resourcetypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.contributor.sponsorMechatronic Engineering research Group of the Universidad Mariana
dc.contributor.sponsorUniversidad de Caldas
dc.contributor.sponsorShandong Academy of Sciences, SDAS
dc.identifier.doi10.18517/ijaseit.10.2.10814
dc.identifier.instnameUniversidad Mariana
dc.identifier.local10814
dc.identifier.reponameRepositorio Clara de Asis
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085167213&doi=10.18517%2fijaseit.10.2.10814&partnerID=40&md5=510fd418b7fe31fc9b3ee17d7d06a8ca
dc.relation.citationendpage437
dc.relation.citationstartpage430
dc.relation.citationvolume10
dc.relation.iscitedby3
dc.relation.referencesMedina Gonzalez P., Evaluación de parámetros cinemáticos de marcha confortable y máxima en adultos mayores válidos chilenos, Fisioterapia, 38, 6, pp. 286-294, (2016)
dc.relation.referencesGuzik A., Druzbicki M., Application of the Gait Deviation Index in the analysis of post-stroke hemiparetic gait, J. Biomech, 99, (2020)
dc.relation.referencesCorrea K.P., Devetak G.F., Martello S.K., de Almeida J.C., Pauleto A.C., Manffra E.F., Reliability and Minimum Detectable Change of the Gait Deviation Index (GDI) in post-stroke patients, Gait Posture, 53, pp. 29-34, (2017)
dc.relation.referencesBarroso F.O., Et al., Combining muscle synergies and biomechanical analysis to assess gait in stroke patients, J. Biomech, 63, pp. 98-103, (2017)
dc.relation.referencesBassile C.C., Hayes S.M., Gait Awareness, Stroke Rehabilitation, pp. 194-223, (2016)
dc.relation.referencesDarter B.J., Webster J.B., Principles of Normal and Pathologic Gait, (2019)
dc.relation.referencesDuraffourg C., Bonnet X., Dauriac B., Pillet H., Real time estimation of the pose of a lower limb prosthesis from a single shank mounted IMU, Sensors (Switzerland), 19, 13, (2019)
dc.relation.referencesPhinyomark A., Osis S.T., Ferber R., Analysis Of Big Data In Running Biomechanics: Application of Multivariate Analysis And Machine Learning Methods, (2016)
dc.relation.referencesCalderita L.V., Bandera J.P., Bustos P., Skiadopoulos A., Model-based reinforcement of kinect depth data for human motion capture applications, Sensors (Switzerland), 13, 7, pp. 8835-8855, (2013)
dc.relation.referencesAbid M., Mezghani N., Mitiche A., Knee joint biomechanical gait data classification for knee pathology assessment: A literature review, Applied Bionics and Biomechanics, 2019, (2019)
dc.relation.referencesYasuda K., Hayashi Y., Tawara A., Iwata H., Development of a vibratory cueing system using an implicit method to increase walking speed in patients with stroke: A proof-of-concept study, ROBOMECH J, 7, 1, (2020)
dc.relation.referencesCalabro R.S., Et al., Walking on the Moon: A randomized clinical trial on the role of lower body positive pressure treadmill training in post-stroke gait impairment, J. Adv. Res, 21, pp. 15-24, (2020)
dc.relation.referencesBuongiorno D., Bortone I., Cascarano G.D., Trotta G.F., Brunetti A., Bevilacqua V., A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease, BMC Med. Inform. Decis. Mak, 19, 9, (2019)
dc.relation.referencesSlijepcevic D., Et al., Automatic Classification of Functional Gait Disorders, IEEE J. Biomed. Heal. Informatics, 22, 5, pp. 1653-1661, (2018)
dc.relation.referencesEltoukhy M., Kuenze C., Oh J., Wooten S., Signorile J., Kinect-based assessment of lower limb kinematics and dynamic postural control during the star excursion balance test, Gait Posture, 58, pp. 421-427, (2017)
dc.relation.referencesvan der Kruk E., Reijne M.M., Accuracy of human motion capture systems for sport applications
dc.relation.referencesstate-of-the-art review, Eur. J. Sport Sci, 18, 6, pp. 806-819, (2018)
dc.relation.referencesParks M.T., Wang Z., Siu K.C., Current Low-Cost Video-Based Motion Analysis Options for Clinical Rehabilitation: A Systematic Review, Physical Therapy, 99, 10, pp. 1405-1425
dc.relation.referencesFilippeschi A., Schmitz N., Miezal M., Bleser G., Ruffaldi E., Stricker D., Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion, Sensors, 17, 6, (2017)
dc.relation.referencesClapes A., Pardo A., Pujol Vila O., Escalera S., Action detection fusing multiple Kinects and a WIMU: An application to inhome assistive technology for the elderly, Mach. Vis. Appl, 29, 5, pp. 765-788, (2018)
dc.relation.referencesZihajehzadeh S., Park E.J., A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture, IEEE Trans. Syst. Man, Cybern. Syst, 47, 6, pp. 927-938, (2017)
dc.relation.referencesZhang J.H., He B.Y., Yang X.S., Zhang W.A., A Review on Wearable Inertial Sensor Based Human Motion Tracking, Zidonghua Xuebao/Acta Automatica Sinica, 45, 8, pp. 1439-1454, (2019)
dc.relation.referencesKumarasiri R., Niroshan A., Lantra Z., Madusanka T., Edussooriya C.U.S., Rodrigo R., Gait Analysis Using RGBD Sensors, 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV2018, pp. 460-465, (2018)
dc.relation.referencesPlantard P., Hubert H.P., Multon F., Filtered pose graph for efficient kinect pose reconstruction, Multimed. Tools Appl, 76, 3, pp. 4291-4312, (2017)
dc.relation.referencesMobini A., Behzadipour S., Foumani M.S., Hand acceleration measurement by Kinect for rehabilitation applications, Sci. Iran, 24, 1, pp. 191-201, (2017)
dc.relation.referencesMuraszkowski A., Et al., Integration of motion capture data acquisition with multibody dynamic simulation software for nordic walking gait analysys, Lecture Notes in Mechanical Engineering, pp. 510-517, (2019)
dc.relation.referencesMartin D.I.H., Putri R., Machbub C., Gait Controllers on Humanoid Robot Using Kalman Filter and PD Controller, 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018, pp. 36-41, (2018)
dc.relation.referencesNair D.S., Jagadanand G., George S., Sensorless direct torque-controlled BLDC motor drive with Kalman filter algorithm, IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2160-2165, (2017)
dc.relation.referencesMegharjun V.N., Talasila V., A Kalman Filter based Full Body Gait Measurement System, 2018 IEEE 3rd International Conference on Circuits, Control, Communication and Computing, (2018)
dc.relation.referencesAbdelhady M., van den Bogert A.J., Simon D., A High-Fidelity Wearable System for Measuring Lower-Limb Kinetics and Kinematics, IEEE Sens. J, 19, 24, pp. 12482-12493, (2019)
dc.relation.referencesYe M., Yang C., Stankovic V., Stankovic L., Cheng S., Gait phase classification for in-home gait assessment, 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 1524-1529, (2017)
dc.relation.referencesMayorca-Torres D., Guerrero-Chapal H., Mejia-Manzano J., Lopez-Mesa D., Peluffo-Ordonez D.H., Salazar-Castro J.A., Multi-target tracking for sperm motility measurement using the kalman filter and JPDAF: Preliminary results, RISTI-Rev. Iber. Sist. e Tecnol. Inf, 2019, E22, pp. 282-294, (2019)
dc.relation.referencesTannous H., Et al., A new multi-sensor fusion scheme to improve the accuracy of knee flexion kinematics for functional rehabilitation movements, Sensors (Switzerland), 16, 11, (2016)
dc.relation.referencesGatt I.T., Allen T., Wheat J., Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system, Sport. Eng, 23, 1, (2020)
dc.relation.referencesBrosseau L., Et al., Intra-and intertester reliability and criterion validity of the parallelogram and universal goniometers for measuring maximum active knee flexion and extension of patients with knee restrictions, Arch. Phys. Med. Rehabil, 82, 3, pp. 396-402, (2001)
dc.relation.referencesQuixada A.P., Onodera A.N., Pena N., Miranda J.G.V., Sa K.N., Validity And Reliability Of Free Software For Bidimentional Gait Analysis, Rev. Pesqui. em Fisioter, 7, 4, pp. 548-557, (2017)
dc.relation.referencesShang Y., Sun X., Yang X., Wang X., Yu Q., A camera calibration method for large field optical measurement, Optik (Stuttg), 124, 24, pp. 6553-6558, (2013)
dc.relation.referencesQi M., Zhang B., Xu Y., Xin H., Cheng G., Linear camera calibration by single image based on distortion correction, ACM International Conference Proceeding Series, pp. 21-25, (2018)
dc.relation.referencesEom K.H., Lee S.J., Kyung Y.S., Lee C.W., Kim M.C., Jung K.K., Improved kalman filter method for measurement noise reduction in multi sensor RFID systems, Sensors, 11, 11, pp. 10266-10282, (2011)
dc.relation.referencesMayorca-Torres D., Caicedo-Eraso J.C., Peluffo-Ordonez D.H., Method for the Improvement of Knee Angle Accuracy Based on Kinect and IMU: Preliminary Results, pp. 184-199, (2019)
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsGait analysis
dc.subject.keywordsIMU
dc.subject.keywordsKalman filter
dc.subject.keywordsKnee-joint angle
dc.subject.keywordsMotion analysis
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.spaArtículo científico
dc.relation.citationissue2


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