Estimadores no lineales: Aplicación del Filtro de Kalman a señales biomecánicas
Keywords:
Filtro de Kalman, Estimadores no lineales, Señales biomecánicas, Tratamiento de señales, Sensores inerciales y magnéticosSynopsis
Este libro es un aporte a la investigación de técnicas de procesamiento de señales digitales reuniendo los elementos teóricos fundamentales para la construcción y el diseño de filtros basados en el algoritmo de Rudolf E. Kalman. Además, se presenta la aplicación de conceptos teóricos a un caso de estudio, el cual presenta el tratamiento y la mejora de las señales biomecánicas producidas por el cuerpo humano. Se determina que la implementación de filtros eficientes proporciona mejores resultados en la generación de la información biomecánica, lo que minimiza los factores que dificultan el uso adecuado de los datos. El diseño e implementación de filtros digitales abre una amplia gama de desarrollos en cuyos beneficios resaltan la posibilidad de crear sistemas de recopilación de información más eficientes y el inicio de otras investigaciones destinadas a mejorar la transmisión de datos, la implementación de algoritmos en sistemas integrados y el procesamiento adecuado de datos digitales.
Chapters
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Preliminares
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1. Fundamentación Teórica para la Implementación de Técnicas de Procesamiento de Señales
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2. Descripción Analítica y Aplicada del Filtro de Kalman
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3. Metodología para la evaluación de rendimiento de estimadores no lineales basados en el filtro de Kalman
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4. Resultados y Discusión
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Conclusiones
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References
O. Šprdlík, Detection and Estimation of Human Movement Using Inertial Sensors: Applications in Neurology. Praga: České vysoké učení technické v Praze. Vypočetní a informační centrum, 2012.
M. Hazewinkel, Ed., Encyclopaedia of Mathematics. Dordrecht: Springer Netherlands, 1995.
E. T. Jaynes and G. L. Bretthorst, Probability theory: the logic of science. Cambridge University Press, 2003.
M. R. Kosorok, Introduction to empirical processes and semiparametric inference. Springer, 2010.
E. L. Lehmann and G. Casella, Theory of Point Estimation. Springer, 1998.
J. O. Berger, Statistical Decision Theory and Bayesian Analysis. New York, NY: Springer New York, 1985.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature”, Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, Jun. 2014.
L. M. Surhone, M. T. Timpledon, and S. F. Marseken, Root Mean Square Deviation. Saarbrücken: Betascript Publishing, 2010.
R. L. Chambers and C. J. Skinner, Analysis of survey data. John Wiley, 2003.
W. H. Press, Numerical recipes: the art of scientific computing. Cambridge University Press, 2007.
C.-E. Särndal, B. Swensson, and J. H. Wretman, Model assisted survey sampling. Springer-Verlag, 1992.
N. A. Campbell and J. B. Reece, Biology. Benjamin Cummings, 2002.
K. J. Rothman, S. Greenland, and T. L. Lash, Modern epidemiology. Lippincott - Williams & Wilkins, 2008.
S. Axler, Linear Algebra Done Right. Cham: Springer International Publishing, 2015.
I. N. Herstein, Topics in algebra. Wiley, 1975.
V. P. Tuzlukov, Signal processing noise. CRC Press, 2002.
S. A. Kassam, Signal Detection in Non-Gaussian Noise. New York, NY: Springer New York, 1988.
D. K. Arrowsmith, C. M. Place, and D. K. Arrowsmith, Dynamical systems: differential equations, maps, and chaotic behaviour. Chapman & Hall, 1992.
M. W. Hirsch and S. Smale, Differential equations, dynamical systems, and linear algebra. Academic Press, 1974.
D. C. Lay, S. R. Lay, and J. McDonald, Linear algebra and its applications.
S. M. Ross, Introduction to probability models. Academic Press, 2010.
R. W. Hamming, The art of probability for scientists and engineers. Addison-Wesley, 1991.
D. E. Knuth, The art of computer programming. Addison-Wesley, 1997.
R. A. Johnson and D. W. Wichern, Applied multivariate statistical analysis. Pearson Prentice Hall, 2007.
R. A. Kent, Data construction and data analysis for survey research. Palgrave, 2001.
A. AghaKouchak, E. Habib, and A. Bárdossy, “Modeling Radar Rainfall Estimation Uncertainties: Random Error Model”, J. Hydrol. Eng., vol. 15, no. 4, pp. 265–274, Apr. 2010.
J. Cohen and J. Cohen, Applied multiple regression/correlation analysis for the behavioral sciences. L. Erlbaum Associates, 2003.
S. Puntanen, “Linear Regression Analysis: Theory and Computing by Xin Yan, Xiao Gang Su”, Int. Stat. Rev., vol. 78, no. 1, pp. 144–144, Apr. 2010.
S. K. Mitter and N. J. Newton, “A Variational Approach to Nonlinear Estimation”, SIAM J. Control Optim., vol. 42, no. 5, pp. 1813–1833, 2003.
R. Manish, U. Inseok Hwang, and A. Professor, “Linear and Non-linear Estimation Techniques: Theory and Comparison”
R. Priemer, Introductory signal processing. World Scientific, 1991.
EMBS Engineering in Medicine and Biology Society, “Biomedical Signal Processing”, 2015. [Online]. Available: https://www.embs.org/about-biomedical-engineering/our-areas-of-research/biomedical-signal-processing/ [Accessed: 02-May-2018].
H.-H. Chang and J. M. F. Moura, “Biomedical Signal Processing”, Biomed. Eng. Des. Handb., vol. 2, no. 1, pp. 559–579, 2010.
NY Castings and joe Gatt, “Motion Capture Actors: Body Movement Tells the Story”, 2016. [Online]. Available: https://www.motionanalysis.com/industry/video-games-film-tv/. [Accessed: 02-May-2018].
University Of Michigan, “Movements of the Upper Limb. Learning Modules - Medical Gross Anatomy”, Michigan, 2015.
S. Maruvada and S. S. Bhimji, Anatomy, Upper Limb, Shoulder, Rotator Cuff. StatPearls Publishing, 2018.
N. Lee, “Flexion & Extension: Sagittal Plane Movements. Anatomy Language: Part II”, 2014. [Online]. Available: http://www.nolanlee.com/blog/anatomy-language-part-ii [Accessed: 03-May-2018].
M. T. O’Toole, Mosby’s medical dictionary. Elsevier, 2009.
S. M. Sze, Semiconductor sensors. Wiley, 1994.
OptiTrack, “OptiTrack - Motion Capture Systems”, 2018. [Online]. Available: https://optitrack.com/ [Accessed: 03-May-2018].
Microsoft Corporation, “Meet Kinect for Windows” [Online]. Available: https://developer.microsoft.com/en-us/windows/kinect [Accessed: 02-May-2018].
D. K. Shaeffer, “MEMS inertial sensors: A tutorial overview”, IEEE Commun. Mag., vol. 51, no. 4, pp. 100–109, Apr. 2013.
N. Yazdi, F. Ayazi, and K. Najafi, “Micromachined inertial sensors”, Proc. IEEE, vol. 86, no. 8, pp. 1640–1659, 1998.
Meta Motion, “Gypsy 5TM by Meta Motion” [Online]. Available: http://metamotion.com/ [Accessed: 02-May-2018].
J. B. Angell, S. C. Terry, and P. W. Barth, “Silicon Micromechanical Devices”, Sci. Am., vol. 248, no. 4, pp. 44–55, Apr. 1983.
MEMS Exchange MNX. “What is MEMS Technology?”, 2013. [Online]. Available: https://www.mems-exchange.org/MEMS/what-is.html [Accessed: 02-May-2018].
N. Maluf and K. Williams, Introduction to microelectromechanical systems engineering. Artech House, 2004.
M. J. Madou, Fundamentals of microfabrication and nanotechnology. CRC Press, 2012.
Techtarget, “What is MEMS (micro-electromechanical systems)?” [Online]. Available: https://internetofthingsagenda.techtarget.com/definition/micro-electromechanical-systems-MEMS [Accessed: 02-May-2018].
D. Scharf, “Utah Division of Arts & Museums – State Of Utah Art Collections: MEMS Art”, 2017. [Online]. Available: http://utahdcc.force.com/public/PtlArtifacts [Accessed: 03-May-2018].
A. Khenkin, “Sonic Nirvana: MEMS Accelerometers as Acoustic Pickups in Musical Instruments | Sensors Magazine”, 2009. [Online]. Available: https://www.sensorsmag.com/embedded/sonic-nirvana-mems-accelerometers-as-acoustic-pickups-musical-instruments [Accessed: 03-May-2018].
H. Nyquist, “Thermal Agitation of Electric Charge in Conductors”, Phys. Rev., vol. 32, no. 1, pp. 110–113, Jul. 1928.
R. Kargon, P. Achinstein, and W. T. Kelvin, Kelvin’s Baltimore lectures and modern theoretical physics: historical and philosophical perspectives. MIT Press, 1987.
C. Song, “Commercial vision of silicon based inertial sensors”, in Proceedings of International Solid State Sensors and Actuators Conference (Transducers ’97), vol. 2, pp. 839–842.
EPSON Seikō Epuson Kabushiki-gaisha, “Gyro sensors: How they work and what’s ahead”, 2016. [Online]. Available: https://www5.epsondevice.com/en/information/technical_info/gyro/ [Accessed: 02-May-2018].
M. Looney, “A simple calibration for MEMS gyroscopes”, Analog Devices, 2010. [Online]. Available: https://www.edn.com/design/sensors/4363439/A-simple-calibration-for-MEMS-gyroscopes [Accessed: 02-May-2018].
D. Budker and M. Romalis, “Optical magnetometry”, Nat. Phys., vol. 3, no. 4, pp. 227–234, Apr. 2007.
Laboratorio de Sensores y detectores SanDLab. Politecnico de Milán., “Laboratorio de Sensores y detectores” [Online]. Available: http://risorse.dei.polimi.it/sensorlab/Research.php/ [Accessed: 03-May-2018].
J. Lenz and S. Edelstein, “Magnetic sensors and their applications”, IEEE Sens. J., vol. 6, no. 3, pp. 631–649, Jun. 2006.
J. J. Uicker, G. R. Pennock, and J. E. Shigley, Theory of machines and mechanisms.
InvenSense, “TDK Invensense MPU-9150” [Online]. Available: https://www.invensense.com/products/motion-tracking/9-axis/mpu-9150/ [Accessed: 02-May-2018].
J. van Leeuwen, Handbook of theoretical computer science. Elsevier, 1990.
G. Brassard and P. Bratley, Fundamentos de algoritmia. Prentice Hall, 1997.
A. Mohr, “Quantum Computing in Complexity Theory and Theory of Computation”
S. McConnell, Code complete: A Practical Handbook of Software Construction, Second Edition. Microsoft Press, 2004.
D. Du and K.-I. Ko, Theory of computational complexity. John Wiley & Sons Inc, 2000.
E. Gamma, Design patterns: elements of reusable object-oriented software. Addison-Wesley, 1995.
K. B. Bruce, Foundations of object-oriented languages: types and semantics. MIT Press, 2002.
J. Kukunas, Power and performance: software analysis and optimization. MK Publishing, 2014.
K. D. Stetzel, L. L. Aldrich, M. S. Trimboli, and G. L. Plett, “Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter”, J. Power Sources, vol. 278, pp. 490–505, Mar. 2015.
W. Khoder and B. Jida, “A Quaternion Scaled Unscented Kalman Estimator for Inertial Navigation States Determination Using INS/GPS/Magnetometer Fusion”, J. Sens. Technol., vol. 4, no. 2, pp. 101–117, Apr. 2014.
E. Cuevas, D. Zaldivar, and R. Rojas, “Kalman filter for vision tracking”, 2005.
S. Dablemont, S. Van Bellegem, and M. Verleysen, “Forecasting high and low of financial time series by particle filters and Kalman filters” 2007.
T. Lacey and N. A. Thacker, “Tutorial: The Kalman Filter”, 1st ed., Manchester, 1998, pp. 133–140.
L. Kleeman, “Understanding and Applying Kalman Filtering”, Dep. Electr. Comput. Syst. Eng., 2013.
R. Faragher, “Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes]”, IEEE Signal Process. Mag., vol. 29, no. 5, pp. 128–132, Sep. 2012.
B. Esme, “Kalman Filter For Dummies”, 2009. [Online]. Available: http://bilgin.esme.org/BitsAndBytes/KalmanFilterforDummies [Accessed: 02-May-2018].
H. Levys, “The Extended Kalman Filter: An Interactive Tutorial for Non-Experts”, 2010. [Online]. Available: https://home.wlu.edu/~levys/kalman_tutorial/ [Accessed: 02-May-2018].
A. A. I. Lange, “Fast Kalman Filtering of the GPS Carrier-Phases for Mobile Positioning and Atmospheric Tomography”, Sci. Fundam. Asp. Galileo Program., 2009.
A. Monfort, J.-P. Renne, and G. Roussellet, “A Quadratic Kalman Filter”, J. Econom., vol. 187, no. 1, pp. 43–56, Jul. 2015.
G. Evensen, “The Ensemble Kalman Filter: theoretical formulation and practical implementation”, Ocean Dyn., vol. 53, no. 4, pp. 343–367, Nov. 2003.
S. Haykin and I. Arasaratnam, “Cubature Kalman Filters”, IEEE Trans. Automat. Contr., vol. 54, no. 6, pp. 1254–1269, 2009.
W. Dang and L. L. Scharf, “Extensions to the Theory of Widely Linear Complex Kalman Filtering”, May 2011.
R. E. Kalman and R. S. Bucy, “New Results in Linear Filtering and Prediction Theory”, J. Basic Eng., vol. 83, no. 1, p. 95, Mar. 1961.
B. A. McElhoe, “An Assessment of the Navigation and Course Corrections for a Manned Flyby of Mars or Venus”, IEEE Trans. Aerosp. Electron. Syst., vol. AES-2, no. 4, pp. 613–623, Jul. 1966.
S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics. MIT Press, 2005.
Y. Cao, “Learning the Unscented Kalman Filter”, MathWorks, 2010. [Online]. Available: https://la.mathworks.com/matlabcentral/fileexchange/18217-learning-the-unscented-kalman-filter [Accessed: 02-May-2018].
G. E. Farin and D. Hansford, Mathematical principles for scientific computing and visualization. AK Peters, 2008.
S. J. Julier and J. K. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems”
G. A. Terejanu, “Unscented Kalman Filter Tutorial”, Dep. Comput. Sci. Eng., 2009.
E. A. Wan and R. van der Merwe, “The Unscented Kalman Filter”, in Kalman Filtering and Neural Networks, New York, USA: John Wiley & Sons, Inc., pp. 221–280.
S. J. Julier and J. K. Uhlmann, “Unscented Filtering and Nonlinear Estimation”, Proc. IEEE, vol. 92, no. 3, pp. 401–422, Mar. 2004.
M. Callejas, M. A. Vélez-Guerrero, A. F. Ruíz-Olaya, and R. M. Gutiérrez, Architecture proposal for a support system to upper limb telerehabilitation by capturing biomechanical signals, vol. 24, no. 40. Universidad Pedagógica y Tecnológica de Colombia, 2015.
N. Xiong and P. Svensson, “Multi-sensor management for information fusion: issues and approaches”, Inf. Fusion, vol. 3, no. 2, pp. 163–186, Jun. 2002.
B. Salina and P. Malathi, “An Efficient Data Fusion Architecture for Location Estimation Using FPGA”, Intl. Journal of Eng. Research & Tech. (IJERT), vol. 3, no. 1, pp. 2634-2639, 2014.
A. Xu, “2-Step Temporal Bayesian Networks (2TBN): Filtering, Smoothing, and Beyond”, School of Computer Science, McGill University, Montreal, Canada.
T. Reineking, “Particle filtering in the Dempster–Shafer theory”, Intl. Journal of Approximate Reasoning, vol. 52, no. 8, pp. 1124-1135, 2011.
M. Grewal and A. Andrews, Kalman filtering. New York: Wiley, 2001.
Y. Cao, “Learning the Extended Kalman Filter”, MathWorks, 2008. [Online]. Available: https://la.mathworks.com/matlabcentral/fileexchange/18189-learning-the-extended-kalman-filter [Accessed: 02-May-2018].
TowerPro, “SG90 Digital | Tower Pro”, 2015. [Online]. Available: http://www.towerpro.com.tw/product/sg90-7/ [Accessed: 03-May-2018].
S. Arora and B. Barak, Computational Complexity: A Modern Approach. New Jersey: Princeton University, 2015.
R. W. Elliott, “Kiviat-graphs as a means for displaying performance data for on-line retrieval systems”, J. Am. Soc. Inf. Sci., vol. 27, no. 3, pp. 178–182, May 1976.
SourceMonitor, “SourceMonitor V3.5”, 2018. [Online]. Available: https://www.campwoodsw.com/sourcemonitor.html [Accessed: 03-May-2018].
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978-958-660-351-5
