يعرض 1 - 20 نتائج من 203 نتيجة بحث عن '"Movimiento humano"', وقت الاستعلام: 0.58s تنقيح النتائج
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    المساهمون: Hamburger Fernández, Álvaro Andrés

    وصف الملف: 67 páginas; application/pdf

    Relation: Ballesteros, S. (1982). El esquema corporal. TEA Ediciones. Bernstein, N. (1967). The coordination and regulation of movement. Pergamon Press. Bolaños, D. y Gámez, R. (2006). Cuerpo, Movimiento y Comunidad. Escenarios para crecer y socializarse. Universidad del Valle. Bolaños, D. (2010). Desarrollo Motor, Movimiento e Interacción. Kinesis. Brunner, J. (1964), The course of cognitive grown. American Psychologist. Clark, J.E; & Whitall, J. (1989). What is motor development? Tho lessons ofhistory. Cobos, P. (1995). El desarrollo psicomotor y sus alteraciones. Manual práctico para evaluarlo y favorecerlo. (pp.17-45) Pirámide. Cohen, R.G. & Rosenbaum, D.A. Where objects are grasped reveals how graps are planned: generalion and recall of motor plans. Experimental Brain Research. (pp. 35-49) Florido, C. (2014). Anatomía Humana. Manual de laboratorio. Universidad Nacional de Colombia. Grimaldos, P. (2016). Características de la anatomía infantil en el desarrollo motor de cero a nueve meses de edad. Universidad Nacional de Colombia. Haibach, P., Reid, G. &COLLIER, D. (2017). Aprendizaje y Desarrollo Motor. Kinesis. Halverson, L.D. (1970). Research in motor develop ment. Implications for program en early childhood education. Paper presented at the Midwest Association for Health, Physical Education and Recreation, Chicago. Hernández, R. (1999). Morfología Funcional Deportiva. Kinesis. Illingworth, R.S. (1985). El niño normal. El Manual Moderno. Kendall, F. & otros. (2007). Kendall´s Músculos Pruebas funcionales. Postura y Dolor. (5 ed. 55 – 74) Marban Libros. Knudson, D. (2007). Fundamentals of biomechanics (2da. Ed.). Springer. (2007). Le Boulch, J. (1999). El desarrollo psicomotor del nacimiento hasta los 6 años. Paidós. López, S. F. (1994). Desarrollo Motor. Universidad de Murcia. Maganto, C. & Cruz, S. (2000). Desarrollo Físico y Psicomotor en la Etapa Infantil. Bermúdez A. (coord.). (2004). Manual de psicología infantil: aspectos evolutivos e intervención psicopedagógica (PONER EN CRU SIVA). (pp. 27 - 64). Biblioteca Nueva. Mora, J. & Palacios, J. (1990). Crecimiento físico y desarrollo psicomotor has ta los 2 años. González, J., Marchesi, A. & Coll, c. (comp.). Desarrollo psicológico y educación (PONER EN CURSIVA). (pp. 81 - 102). Alianza. Moore, K., & Dalley, A. (2006). Anatomía con orientación clínica (5ta. ed.). Mé dica Panamericana. Muñoz, C. I. (2001). Psicomotricidad. Facultad de Salud, Escuela Rehabilita ción Humana, Programa académico de Fisioterapia. Santiago de Cali, Colombia. 2001. Muñoz, L. A. (2003). Educación Psicomotriz. Kinesis. Nelson, W.E., Vaughan, V.C. & Mckay, R.J. (1983). Tratado de Pediatría. (8ª ed.) Salvat Editores. Norkin, C. y Levangie, P. (1992). Join Structure & Function. F.A. Davis. Papalia, D. (1998). Psicología del Desarrollo. (7 ed.) McGraw Hill. Piaget, J. (1982). Nascimiento da inteligencia na crianca. LTC Editora. Picq, L. & Vayer, P. (1997). Educación psicomotriz y retraso mental. Editorial Científico Médica. Piper, M., & Darrah, J. (1984). Motor Assessment of the Developing Infant. Saunders. Proffitt, D.R., & otros. (1995). Perceiving geographical slant. Psychonomic Bulletin & Review Rice, J.P. (1997). Desarrollo humano. Estudio del ciclo vital. Prentice Hall His panoamericana. Rigal, R., Paoletti, R. & Portmann M. (1979). Motricidad: Aproximación psicofi siológica. Augusto Pila Teleña. Rosenbaum, D.A. (2010). Human motor control (2da. ed.). Academic Press. Rumelhart, D.E. & Norman, D.A. (1982). Simulating a skilled typist: A study of skilled cognitive motor performance. Cognitive Science. Santos, L. (2000). Síntesis de anatomía humana. Ediciones Universidad de Salamanca. Schmidt, R.A & Lee, T.D. (2005). Motor control and feruning: A behavioral em phasis. (4ta. Ed.) Human Kinetics. Spirduso, W., Francis, K., & MacRae, P. (2005). Physical dimensions of aging. Human Kinetics. Thelen, E., & otros (1982). Effects of body build and arousal on dewborn inafant stepping. Development Psycobiology; Barajas Ramón, Y., Pájaro Olivo, F.E., & Torres Plata, J.M. (2022). Características de desarrollo psicomotor. Universidad de San Buenaventura Cartagena.; https://hdl.handle.net/10819/11340

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    المصدر: Ciencia, Docencia y Tecnología Suplemento; Vol. 13 No. 15 (2023): Ciencia, Docencia y Tecnología Suplemento ; Ciencia, Docencia y Tecnología Suplemento; ##issue.vol## 13 ##issue.no## 15 (2023): Ciencia, Docencia y Tecnología Suplemento ; Ciencia, Docencia y Tecnología Suplemento; Vol. 13 Núm. 15 (2023): Ciencia, Docencia y Tecnología Suplemento ; Ciencia, Docencia y Tecnología Suplemento; Vol. 13 N.º 15 (2023): Ciencia, Docencia y Tecnología Suplemento ; 2250-4559

    وصف الملف: application/pdf

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    Dissertation/ Thesis

    المؤلفون: González Rodríguez, Leticia

    المساهمون: López Rodríguez, Antonio Miguel, Ingeniería Eléctrica, Electrónica, de Computadores y Sistemas, Departamento de

    مصطلحات موضوعية: Movimiento humano, Monitorización, Evaluación, Predicción

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    Academic Journal

    المؤلفون: Pulgarín Giraldo, Juan Diego

    المساهمون: Álvarez Meza, Andrés Marino, Castellanos Domínguez, César Germán, Grupo de Control y Procesamiento Digital de Señales

    وصف الملف: application/pdf

    Relation: Althloothi, S., Mahoor, M. H., Zhang, X., and Voyles, R. M. (2014). Human activity recognition using multi-features and multiple kernel learning. Pattern Recognition, 47(5):1800 - 1812.; Alvarez, M. A., Rosasco, L., and Lawrence, N. D. (2012). Kernels for vector-valued functions: A review. Found. Trends Mach. Learn., 4(3):195-266.; Alvarez-Meza, A. M., C ardenas-Peña, D., and Castellanos-Dominguez, G. (2014). Unsupervised kernel function building using maximization of information potential variability. In CIARP 2014, pages 335-342. Springer International Publishing.; Anantasech, P. and Ratanamahatana, C. A. (2019). Enhanced weighted dynamic time warping for time series classifi cation. In Third International Congress on Information and Communication Technology, pages 655-664. Springer.; Berlinet, A. and Thomas-Agnan, C. (2011). Reproducing kernel Hilbert spaces in probability and statistics. Springer Science & Business Media.; Bicego, M. and Murino, V. (2004). Investigating hidden markov models' capabilities in 2d shape classifi cation. IEEE Trans. Pattern Anal. Mach. Intell., 26(2):281-286.; Bicego, M., Murino, V., and Figueiredo, M. A. (2004). Similarity-based classification of sequences using hidden markov models. Pattern Recognition, 37(12):2281-2291.; Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.; Blanchard, G., Bousquet, O., and Zwald, L. (2007). Statistical properties of kernel principal component analysis. Machine Learning, 66(2-3):259-294.; Callejas-Cuervo, M., Alvarez, J., and Alvarez, D. (2016). Capture and analysis of biomechanical signals with inertial and magnetic sensors as support in physical rehabilitation processes. In BSN 2016 - 13th Annual Body Sensor Networks Conference, pages 119-123.; Callejas-Cuervo, M., Gutierrez, R., and Hernandez, A. (2017). Joint amplitude mems based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study. Journal of Bodywork and Movement Therapies, 21(3):574-581.; Callejas-Cuervo, M., Pineda-Rojas, J. A., and Daza-Wittinghan, W. A. (2020). Analysis of ball interception velocity in futsal goalkeepers. Journal of Human Sport and Exercise, 15:S735-S747.; Carnegie-Mellon University (2003). CMU graphics lab: Carnegie-Mellon motion capture (mocap) database. http://mocap.cs.cmu.edu. Accessed: 2020-09-14.; Chen, B., Zhao, S., Zhu, P., and Principe, J. C. (2012). Quantized kernel least mean square algorithm. IEEE Transactions on Neural Networks and Learning Systems, 23(1):22-32.; Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., and Batista, G. (2015). The UCR time series classi fication archive. www.cs.ucr.edu/~eamonn/time_series_data/.; Cho, W., Kim, S., and Park, S. (2017). Human action recognition using hybrid method of hidden Markov model and Dirichlet process gaussian mixture model. Advanced Science Letters, 23(3):1652-1655.; Cortes, C., Mohri, M., and Rostamizadeh, A. (2012). Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res., 13(1):795-828.; Delgado-Garc a, G., Vanrenterghem, J., Muñoz Garc ía, A., Molina-Molina, A., and Soto-Hermoso, V. (2019). Does stroke performance in amateur tennis players depend on functional power generating capacity? Journal of Sports Medicine and Physical Fitness, 59(5):760-766; Duin, R. P. W. and Pekalska, E. (2005). Dissimilarity Representation For Pattern Recognition, The: Foundations And Applications, volume 64 of Machine Perception and Arti ficial Intelligence. World Scienti c Publishing Co., Inc.; Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A. (2019). Deep learning for time series classifi cation: a review. Data Mining and Knowledge Discovery, pages 1-47.; Field, M., Stirling, D., and Pan, Z. (2015). Recognizing human motions through mixture modeling of inertial data. Pattern Recognition, 48(8):2394 - 2406.; Fukumizu, K., Bach, F., and Gretton, A. (2007). Statistical consistency of kernel canonical correlation analysis. Journal of Machine Learning Research, 8:361-383.; Fukumizu, K., Bach, F. R., and Jordan, M. I. (2004). Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. J. Mach. Learn. Res., 5:73-99.; Garci a-Vega, S., Alvarez-Meza, A. M., and Castellanos-Dom inguez, G. (2013). Mocap data segmentation and classi fication using kernel based multi-channel analysis. In Ruiz-Shulcloper, J. and Sanniti di Baja, G., editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - CIARP 2013, Proceedings, pages 495-502, Cham. Springer Berlin Heidelberg.; Gil-Gonzalez, J., Alvarez-Meza, A., and Orozco-Gutierrez, A. (2018). Learning from multiple annotators using kernel alignment. Pattern Recognition Letters, 116:150 - 156.; Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012). A kernel two-sample test. J. Mach. Learn. Res., 13:723-773.; Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B. (2005). Measuring statistical dependence with Hilbert-Schmidt norms. In Jain, S., Simon, H. U., and Tomita, E., editors, Algorithmic Learning Theory, pages 63-77, Berlin, Heidelberg. Springer Berlin Heidelberg.; Han, F., Reily, B., Ho , W., and Zhang, H. (2017). Space-time representation of people based on 3d skeletal data: A review. Computer Vision and Image Understanding, 158:85-105.; ITF (2007). ITF coaches education programme level 2 coaching course. Physical conditioning for tournament players. https://en.coaching.itftennis.com/media/24729/24729.PDF. Accessed: 2014-03-30.; Jebara, T., Kondor, R., and Howard, A. (2004). Probability product kernels. Journal of Machine Learning Research, 5(Jul):819-844.; Jeong, Y.-S., Jeong, M. K., and Omitaomu, O. A. (2011). Weighted dynamic time warping for time series classi fication. Pattern recognition, 44(9):2231-2240.; Kadu, H. and Kuo, C.-C. J. (2014). Automatic human mocap data classifi cation. IEEE Transactions on Multimedia, 16(8):2191-2202.; Kotsifakos, A. (2014). Case study: Model-based vs. distance-based search in time series databases. In Exploratory Data Analysis (EDA) Workshop in SIAM International Conference on Data Mining (SDM).; Landlinger, J., Lindinger, S., Stoggl, T., Wagner, H., and Muller, E. (2010). Key factors and timing patterns in the tennis forehand of di fferent skill levels. Journal of sports science & medicine, 9:643-651.; Marco, V. R., Young, D. M., and Turner, D. W. (1987). The euclidean distance classi fier: an alternative to the linear discriminant function. Communications in Statistics-Simulation and Computation, 16(2):485-505.; Microsoft Research (2009). MSR action data set. http://research.microsoft.com/en-us/um/people/zliu/actionrecorsrc/. Accessed: 2020-09-14.; Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.; Niu, G., Dai, B., Yamada, M., and Sugiyama, M. (2014). Information-theoretic semi-supervised metric learning via entropy regularization. Neural computation, 26(8):1717-1762.; Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., and Bajcsy, R. (2014). Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition. J. Visual Communication and Image Representation, 25(1):24-38.; OptiTrack™(2009). Arena ™motion capture software tutorials. https://youtu.be/gjrsIBf124s?list=PL417F2589E95EFCA9. Accessed: 2020-02-17.; Orozco-Alzate, M., Duin, R., and Castellanos-Domínguez, G. (2009). A generalization of dissimilarity representations using feature lines and feature planes. Pattern Recognition Letters, 30(3):242-254.; Park, M., Jitkrittum, W., and Sejdinovic, D. (2016). K2-abc: Approximate bayesian computation with kernel embeddings. volume 51 of Proceedings of Machine Learning Research, pages 398-407, Cadiz, Spain. PMLR.; Perez-Cruz, F., Van Vaerenbergh, S., Murillo-Fuentes, J., Lazaro-Gredilla, M., and Santamaria, I. (2013). Gaussian processes for nonlinear signal processing: An overview of recent advances. IEEE Signal Processing Magazine, 30(4):40-50.; Principe, J. C. (2010). Information theoretic learning: Renyi's entropy and kernel perspectives. pages 1-45, New York, NY. Springer New York.; Pulgarin-Giraldo, J. D., Alvarez-Meza, A. M., Melo-Betancourt, L. G., Ramos-Bermudez, S., and Castellanos-Dominguez, G. (2016). A similarity indicator for di fferentiating kinematic performance between qualifi ed tennis players. In Beltran-Castanon, C. A., Nystrom, I., and Famili, F., editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - CIARP 2016, Lima, Per u, Proceedings, pages 309-317. Springer International Publishing.; Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286.; Ravet, T., Tilmanne, J., and d'Alessandro, N. (2014). Hidden markov model based real-time motion recognition and following. In Proceedings of the 2014 International Workshop on Movement and Computing, MOCO '14, page 82:87, New York, NY, USA. ACM.; Schölkopf, B. and Smola, A. J. (2002). Learning with Kernels. The MIT Press, Cambridge, MA, USA.; Shimizu, T., Hachiuma, R., Saito, H., Yoshikawa, T., and Lee, C. (2019). Prediction of future shot direction using pose and position of tennis player. pages 59-66.; Smola, A., Gretton, A., Song, L., and Schölkopf, B. (2007). Algorithmic learning theory: 18th international conference, alt 2007, sendai, japan, october 1-4, 2007. proceedings. pages 13-31, Berlin, Heidelberg. Springer Berlin Heidelberg.; Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B., and Lanckriet, G. (2012). On the empirical estimation of integral probability metrics. Electronic Journal of Statistics, 6:1550-1599.; Sriperumbudur, B. K., Gretton, A., Fukumizu, K., Sch olkopf, B., and Lanckriet, G. R. (2010). Hilbert space embeddings and metrics on probability measures. J. Mach. Learn. Res., 11:1517- 1561.; Tanisaro, P. and Heidemann, G. (2016). Time series classi fication using time warping invariant echo state networks. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 831-836. IEEE.; Theodoridis, S. and Koutroumbas, K. (2009). Pattern Recognition. Academic Press, Boston, fourth edition edition.; Valencia, E. A. and Alvarez, M. A. (2017). Short-term time series prediction using Hilbert space embeddings of autoregressive processes. Neurocomputing, 266:595 - 605.; Van Der Maaten, L. and Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9:2579-2625.; Wang, J. M., Fleet, D. J., and Hertzmann, A. (2008). Gaussian process dynamical models for human motion. IEEE Trans. Pattern Anal. Mach. Intell., 30(2):283-298.; Wang, S., Hou, Y., Li, Z., Dong, J., and Tang, C. (2018). Combining convnets with hand-crafted features for action recognition based on an hmm-svm classi er. Multimedia Tools and Applications, 77(15):18983-18998.; Whiteside, D., Elliott, B., Lay, B., and Reid, M. (2013). A kinematic comparison of successful and unsuccessful tennis serves across the elite development pathway. Human Movement Science, 32(4):822 - 835.; Winter, D. A. (2009). Biomechanics and Motor Control of Human Movement. John Wiley Sons, fourth edition.; Wu, Y. (2012). Mining actionlet ensemble for action recognition with depth cameras. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR '12, pages 1290-1297, Washington, DC, USA. IEEE Computer Society.; Zafari, A., Milla, R. Z., and Izquierdo-Verdiguier, E. (2020). A multi-scale random forest kernel for land cover classi fication. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.; Zago, M., Codari, M., Iaia, F., and Sforza, C. (2017). Multi-segmental movements as a function of experience in karate. Journal of Sports Sciences, 35(15):1515-1522.; Zeng, J., Duan, J., and Wu, C. (2010). A new distance measure for hidden markov models. Expert systems with applications, 37(2):1550-1555.; Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., and Chen, D.-S. (2019). A comprehensive survey of vision-based human action recognition methods. Sensors, 19(5).; Dai, Z., Alvarez, M., and Lawrence, N. (2017). Efficient modeling of latent information in supervised learning using gaussian processes. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems NIPS 2017, volume 30, pages 5131-5139. Curran Associates, Inc.; Fontes, A., Rego, J., Martins, A., Silveira, L., and Principe, J. (2017). Cyclostationary correntropy: Definition and applications. Expert Systems with Applications, 69:110--117.; Fukuchi, R., Fukuchi, C., and Duarte, M. (2017). A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics. PeerJ, 2017(5):3298. Figshare dataset available at https://doi.org/10.6084/m9.figshare.4543435.v4; Liu, W., Principe, J. C., and Haykin, S. (2010). Kernel Adaptive Filtering: A Comprehensive Introduction. John Wiley & Sons, Inc., 1st edition.; Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B. (2017). Kernel Mean Embedding of Distributions: A Review and Beyond, volume 10:1-2 of Foundations and Trends® in Machine Learning. Now Publishers Inc.; Müller, M. (2007). Dynamic time warping. In Information Retrieval for Music and Motion, pages69–84. Springer, Berlin, Heidelberg.; Pulgarin-Giraldo, J. D., Alvarez-Meza, A. M., Vaerenbergh, S. V., Santamaria, I., and Castellanos-Dominguez, G. (2019). Analysis and classification of MoCap data by Hilbert space embedding based distance and multikernel learning. In Vera-Rodriguez, R., Fierrez, J., and Morales, A., editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - CIARP 2018, Madrid, Spain, Proceedings, pages 186-193, Cham. Springer International Publishing.; Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian Processes for Machine Learning, volume 2 of Adaptive Computation and Machine Learning. The MIT Press.; Van Vaerenbergh, S., Lazaro-Gredilla, M., and Santamaria, I. (2012). Kernel recursive least-squares tracker for time-varying regression. IEEE Transactions on Neural Networks and Learning Systems, 23(8):1313-1326.; Van Vaerenbergh, S. and Santamaria, I. (2013). A comparative study of kernel adaptive filtering algorithms. In 2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), pages 181-186. Software available at https://github.com/steven2358/kafbox/; Pulgarin-Giraldo, J.D., Ruales-Torres, A.A., Alvarez-Meza, A.M., Castellanos-Dominguez, G. (2017). Relevant kinematic feature selection to support human action recognition in MoCap data. In Ferrandez-Vicente, J. M., Alvarez-Sanchez, J. R., de la Paz-Lopez. F., Toledo-Moreo, J. and Adeli, H., editors, Biomedical Applications Based on Natural and Artificial Computing – IWINAC 2017, Proceedings, pages 501-509, Cham. Springer International Publishing.; Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Van Vaerenbergh, S., Santamaria, I., Castellanos-Dominguez, G. (2019). MoCap multichannel time series representation and relevance analysis by kernel adaptive filtering and multikernel learning oriented to action recognition tasks. In Valenzuela, O., Rojas, F., Pomares, H. and Rojas, I., editors, International Conference on Time Series and Forecasting - ITISE 2018, Granada, Spain. Proceedings, pages 1316-1327.; https://repositorio.unal.edu.co/handle/unal/78904

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    وصف الملف: application/pdf

    Relation: Adamová B., Kutilek P., Cakrt O., Svoboda Z., Viteckova S., Smrcka P. Quantifying postural stability of patients with cerebellar disorder during quiet stance using three-axis accelerometer. Biomed. Signal Process. Control, vol. 40. pp. 378–384, 2018.; Azman A.M, Kuga H., Sagawa K., Nagai, C. Fastest Gait Parameters Estimation Precision Comparison Utilizing High-Sensitivity and Low-Sensitivity Inertial Sensor. Springer, Singapore. pp. 79–84, 2018.; Baek S., Kim M. Real-Time Tracking IDs and Joints of Users. VII International Conference on Network, Communication and Computing, pp. 221-226, 2018.; Bevilacqua V. et al. A comprehensive approach for physical rehabilitation assessment in multiple sclerosis patients based on gait analysis BT. Advances in Intelligent Systems and Computing, vol. 590. Springer Verlag, Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy. pp. 119–128, 2018.; Bilesan A., Behzadipour S., Tsujita T., Komizunai S., Konno A. Markerless Human Motion Tracking Using Microsoft Kinect SDK and Inverse Kinematics. 12th Asian Control Conference, ASCC 2019, vol. 12, p. 149757, 2019.; Budzyńska A., Jagielski M., Żyliński M., Cybulski G., Niewiadomski W. Verification of Selected Gait Parameters Derived from Inertial Sensors Using Simple Smartphone Based Optical System. Advances in Intelligent Systems and Computing, vol. 1044, pp. 87-94, 2020.; Callejas-Cuervo M., Gutierrez R.M., Hernandez A.I. Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study. J. Bodyw. Mov. Ther., vol. 21, no. 3. pp. 574–581, 2017.; Charlton J., Xia H., Shull P., Hunt M. Validity and reliability of a shoe-embedded sensor module for measuring foot progression angle during over-ground walking. Journal of Biomechanics, vol. 89, pp. 123-127, 2019.; Cuervo M.C., Olaya A.F., Salamanca R.M. Biomechanical motion capture methods focused on tele-physiotherapy. 2013 Pan American Health Care Exchanges PAHCE. pp. 1–6, 2013.; Dawe R., et al. Expanding instrumented gait testing in the community setting: A portable, depth-sensing camera captures joint motion in older adults. PLOS ONE, vol. 14, no. 5, p. e0215995, 2019.; Deligianni F., Wong C., Lo B., Yang, G.-Z. A fusion framework to estimate plantar ground force distributions and ankle dynamics. Inf. Fusion, vol. 41. pp. 255–263, 2018.; El Maachi I., Bilodeau G., Bouachir W. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Systems with Applications, vol. 143, p. 113075, 2020.; Fleron M.K., Ubbesen N.C.H., Battistella F., Dejtiar D.L., Oliveira A.S. Accuracy between optical and inertial motion capture systems for assessing trunk speed during preferred gait and transition periods, Sport. Biomech. pp. 1–12, 2018.; Hanawa H., et al. Validity of inertial measurement units in assessing segment angles and mechanical energies of elderly persons during sit-to-stand motion. 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2019.; Iosa M., Picerno P., Paolucci S., Morone G. Wearable inertial sensors for human movement analysis. Expert Rev. Med. Devices, vol. 4444. pp. 1–19, 2017.; Kim M., Lee D. Wearable inertial sensor based parametric calibration of lower-limb kinematics. Sensors Actuators, A Phys., vol. 265. pp. 280–296, 2017.; LeMoyne R., Mastroianni T. The rise of inertial measurement units. Smart Sensors, Measurement and Instrumentation, vol. 27. Springer International Publishing, Department of Biological Sciences, Center for Bioengineering Innovation, Northern Arizona University, Flagstaff, AZ, United States. pp. 45–58, 2018.; McGrath T., Fineman R., Stirling L. An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors. Sensors, vol. 18, no. 6. p. 1882, 2018.; Marxreiter F., et al. Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease. Journal of Neurology. vol. 265, no. 11. pp. 2656-2665, 2019.; Park S., Ho Y., Chun M., Choi J., Moon Y. Measurement and Analysis of Gait Pattern during Stair Walk for Improvement of Robotic Locomotion Rehabilitation System. Applied Bionics and Biomechanics, vol. 2019, pp. 1-12, 2019.; Petraglia F., Scarcella L., Pedrazzi G., Brancato L., Puers R., Costantino C. Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis. European Journal of Physical and Rehabilitation Medicine, vol. 55, no. 2, 2019.; Qiu S., Wang Z., Zhao H., Qin K., Li Z., Hu H. Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion. Inf. Fusion, vol. 39. pp. 108–119, 2018.; Rana S., Dey M., Ghavami M., Dudley S. Non-Contact Human Gait Identification through IR-UWB Edge-Based Monitoring Sensor. IEEE Sensors Journal, vol. 19, no. 20, pp. 9282-9293, 2019.; Ren P., et al. Movement Symmetry Assessment by Bilateral Motion Data Fusion. IEEE Transactions on Biomedical Engineering, vol. 66, no. 1, pp. 225-236, 2019.; Schwartz M.H., Rozumalski A. The gait deviation index: A new comprehensive index of gait pathology. Gait Posture. vol. 28, no. 3. pp. 351–357, 2008.; Shiotani M., Watanabe T., Murakami K., Kuge N. Research on detection method for abnormal gait using three-dimensional thigh motion analysis with inertial sensor. Transactions of Japanese Society for Medical and Biological Engineering, vol. 57, no. 1, pp. 1-7, 2019.; Sprager S., Juric M.B. Inertial Sensor-Based Gait Recognition: A Review. Sensors., vol. 15, no. 9. pp. 22089–22127, 2015.; Sun C., Wang C., Lai W. Gait analysis and recognition prediction of the human skeleton based on migration learning. Physica A: Statistical Mechanics and its Applications, vol. 532, p. 121812, 2019.; Tjhai C., Steward J., Lichti D., O’Keefe K. Using a mobile range-camera motion capture system to evaluate the performance of integration of multiple low-cost wearable sensors and gait kinematics for pedestrian navigation in realistic environments. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS. pp. 294–300, 2018.; Vo N., Tuan A., Van T.V., Vu N., Hau D., Thang N.D. Abnormal Gait Detection and Classification Using Depth Camera. 6th Int.Conf. Dev. Biomed. Eng. Vietnam (BME6), IFMBE Proc. pp. 1–6, 2018.; Wang Y., Cang S., Yu H. A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, vol. 137, pp. 167-190, 2019.; Wagner J., et al. Comparison of two techniques for monitoring of human movements. Meas. J. Int. Meas. Confed., vol. 111. pp. 420–431, 2017.; Watanabe T., Tadano T. An Examination of Stimulation Timing Patterns for Mobile FES Cycling Under Closed-Loop Control of Low Cycling Speed. Converging Clinical and Engineering Research on Neurorehabilitation III, vol. 21, pp. 1106-1110, 2018.; Wolosker N., Nakano L., Rosoky R.A., Puech-Leao P. Evaluation of walking capacity over time in 500 patients with intermittent claudication who underwent clinical treatment. Arch. Intern. Med., vol. 163. pp. 2296–300, 2003.; Wouda F.J., et al. Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors, Front. Physiol., vol. 9. p. 218, 2018.; Whittle M.W. An Introduction to Gait Analysis. 4th ed. Oxford: Butterworth-Heinemann, 2007.; Xie N., Mok P.Y. Investigation on human body movements and the resulting body measurement variations. AHFE 2017 International Conference on Physical Ergonomics and Human Factors, vol. 602, Springer Verlag, The Hong Kong Polytechnic University, Kowloon, Hong Kong. pp. 387–399, 2018.; Xu C., He J., Zhang X., Yao C., Tseng P.-H. Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Inf. Fusion, vol. 41. pp. 243–254, 2018.; Zago M. et al. Gait evaluation using inertial measurement units in subjects with Parkinson’s disease. J. Electromyogr. Kinesiol., vol. 42, pp. 44–48, 2018.; https://revistas.eia.edu.co/index.php/reveia/article/download/1472/1364; Núm. 34 , Año 2020; 11; 34; 17; Revista EIA; https://repository.eia.edu.co/handle/11190/5137; https://doi.org/10.24050/reia.v17i34.1472