Aprendizaje Automático en Aplicaciones Fisioterapéuticas

  • Juan Carlos González-Islas Universidad Tecnológica de Tulancingo
Palabras clave: aprendizaje automático, medicina, fisioterapia, análisis de la marcha

Resumen

El aprendizaje automático (AA) o aprendizaje máquina se centra en el desarrollo de sistemas de análisis que aprenden de datos existentes para predecir o agrupar datos futuros.  El AA se ha desarrollado sustancialmente en años recientes en muchas áreas de la ciencia y de la ingeniería, siendo una de las de mayor interés el área de la medicina. En los ambientes médicos reales existe una gran cantidad de datos de naturaleza multivariable y multidimensional, que requieren analizarse para diagnóstico y tratamiento médico, lo que representa un área de oportunidad importante para el aprendizaje máquina.  Este trabajo presenta una revisión de algunas de las aplicaciones más relevantes del aprendizaje automático, así como las tendencias y expectativas emergentes del mismo. Posteriormente, se hace una descripción de los trabajos más significativos del aprendizaje máquina aplicados a la medicina, haciendo especial énfasis en aplicaciones fisioterapéuticas. Finalmente, un tema de particular interés en el contexto de la medicina física es el análisis de la marcha, por lo que se hace una revisión de los trabajos realizados para dicho propósito.  

Descargas

La descarga de datos todavía no está disponible.

Citas

Ahamed, F., & Farid, F. (2018). Applying Internet of Things and Machine-Learning for Personalized Healthcare: Issues and Challenges. IEEE. International Conference on Machine Learning and Data Engineering (iCMLDE) (págs. 19-21). IEEE.

Ahangar, R. G., & Ahangar, M. F. (2009). Handwritten farsi character recognition using artificial neural network. International Journal of computer Science and Information security, 4(1 & 2 ), 1-3.

Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications, 36(2), 3240-3247., 36(2), 3240-3247.

Anam, K., & Al-Jumaily, A. (2015). A robust myoelectric pattern recognition using online sequential extreme learning machine for finger movement classification. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (págs. 7266-7269). IEEE.

Anderson, T. W. (1962). An introduction to multivariate statistical analysis. New York: Wiley.

Ar, I., & Akgul, Y. S. (2012). A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(6), 1160-1171.

Ayodele, T. O. (2010). Introduction to machine learning. In New Advances in Machine Learning. IntechOpen.

Begg, K., Palaniswami, M., & Owen, B. (2005). Support Vector Machines for Automated Gait Classificatio. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 52(5), 828-838.

Bishop, C. M. (2006). Pattern recognition and machine learning. Singapore: Springer .

Bulbul, H. I., & Unsal, Ö. (2011). Comparison of classification techniques used in machine learning as applied on vocational guidance data. 2011 10th International Conference on Machine Learning and Applications and Workshops. 2, págs. 298-301. IEEE.

Burns, D. M., Leung, N., Hardisty, M., Whyne, C. M., Henry, P., & McLachlin, S. (2018). Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch. Physiological measurement, 7, 075007.

Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. Jama, 318(6), 517-518.

Chang, C. V., Cheng, M. J., & Ma, M. H. (2018). Application of Machine Learning for Facial Stroke Detection. IEEE 23rd International Conference on Digital Signal Processing (DSP) (págs. 1-5). IEEE.

Chang, H. K., Wu, C. T., Liu, J. H., & Jang, J. S. (2018). Using Machine Learning Algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting. Conference on Technologies and Applications of Artificial Intelligence (TAAI) (págs. 1-4). IEEE.

Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011). Convolutional neural network committees for handwritten character classification. International Conference on Document Analysis and Recognition (págs. 1315-1139). IEEE.

Cleophas, T. J., & Zwinderman, A. H. (2013). Machine Learning in Medicine. New York: Springer.

Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: computational and mathematical modeling of neural systems. Cambridge: MIT Press.

Deboeverie, F., Roegiers, S., Allebosch, G., Veela, P., & Philips, W. (2016). Human gesture classification by brute-force machine learning for exergaming in physiotherapy. IEEE Conference on Computational Intelligence and Games (CIG) (págs. 1-7). IEEE.

Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930. Circulation, 132(20), 1920-1930.

Evans, B., & Fisher, D. (1994). Overcoming process delays with decision tree induction. IEEE expert, 9(1), 60-66. IEEE expert, 1, 60-66.

Faisal, M. I., Bashir, S., Khan, Z. S., & Khan, F. (2018). An Evaluation of Machine Learning Classifiers and Ensembles for Early Stage Prediction of Lung Cancer. 3rd International Conference on Emerging Trends in Engineering, Sciences (págs. 1-4). IEEE.

Fatmawati, E., & Wijaya, S. K. (2017). Development Prototype System of Arm's Motor Imagery Utilizing Electroencephalography Signals (EEG) from Emotiv with Probabilistic Neural Network (PNN) as Signal Analysis. 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME) (págs. 179-183). IEEE.

Feigenbaum, E. A. (1961). The simulation of verbal learning behavior. Western join IRE-AIEE-ACM computer conference (pág. 121.132). ACM.

Gilbert, F. J., Astley, S. M., Gillan,, M. G., Agba, O. F., Wallis, M. G., James, J., & Duffy, S. W. (2008). Gilbert, F. J., Astley, S. M., Gillan, M. G., Agbaje, O. F., Wallis, M. G., James, J., ... & Duffy, S. W. (2008). Single reading with computer-aided detection for screening mammography. New England Journal of Medicine, 359(16), 1675-1684.

Gluck, M. A., & Rumelhart, D. E. (2013). Neuroscience and connectionist theory. Psychology Press.

Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99. Machine learning, 3(2), 95-99.

Gonzalez-Islas, J. C., Godinez-Garrido, G., & Gonzalez-Rosas, A. (2018). Sistema mecatrónico para asistencia motriz a niños con discapacidad psicomotriz. Revista de TEcnología y Educación, 2(6), 1-9.

Guillen, M., & Pesantez-Narvaez, J. (2018). Machine Learning and Predictive Modeling for Automobile Insurance Pricing. Anales del Instituto de Actuarios Españoles,, (págs. 123-147).

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Series in Statistics.

Jabbour, K., Riveros, J. F., Landsbergen, D., & Meyer, W. (1988). ALFA: Automated load forecasting assistant. IEEE Transactions on Power Systems, 3, 908-914.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Kavakiotis, I., Tsave, O., Salifoglou, A., Maglave, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 15, 104-116.

Khan, A., Doucette, J. A., Cohen, R., & Lizotte, D. (2012). Integrating machine learning into a medical decision support system to address the problem of missing patient data. International Conference on Machine Learning and Appl. 1, págs. 454-457. IEEE.

Kim, Y. (2018). Aplication of Machine Learning to Antenna Design and Radar Signal Processing: A Review. 2018 International Symposium on Antennas and Propagation (ISAP) (págs. 1-2). IEEE.

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 18-17.

Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 321-332.

Mannini, A., & Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 2, 1154-1175.

Martínez, A. J., Rodríguez-Piñero, P. T., & March, J. H. (2010). Un análisis comparativo de una svm y un modelo logit en un problema de clasificación de asegurados. In Anales del Instituto de Actuarios Españoles, 16, 85-110.

Mazilu, S., Hardegger, M., Zhu, R. D., Tröster, G., Plotnik, .., & Hausdorff, J. M. (2012). Online detection of freezing of gait with smartphones and machine learning techniques. 6th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health) and Workshops (págs. 123-130). IEEE.

McConnell, A. C., Vallejo, M., Moioli, R. C., Brasil, F. L., Secciani, N., Nemitz, M. P., & Stokes, A. A. (s.f.). SOPHIA: soft orthotic physiotherapy hand interactive aid. Frontiers in Mechanical Engineering, 3, 3.

Menéndez, L. A., De Cos Juez, F. J., Lasheras, F., & Riesgo, J. A. (2010). Artificial neural networks applied to cancer detection in a breast screening programme. . Mathematical and Computer Modelling, 52(7-8), 983-991., 52(7,8), 983-991.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13), 1216-1219.

Paluszek, M., & Thomas, S. (2016). MATLAB machine learning. Apress.

Patil, M. A., Patil, R. B., Krishnamoorthy, P., & Jhon, J. (2016). A machine learning framework for auto classification of imaging system exams in hospital setting for utilization optimization. 38th Annual International Conference of the IIEEE Engineering in Medicine and Biology Society (EMBC) (págs. 2423-2426). IEEE.

Patsadu, O., Nukoolkit, C., & Watanapa, B. (2012). Human gesture recognition using Kinect camera. Ninth International Conference on Computer Science and Software Engineering (JCSSE) (págs. 28-32). IEEE.

Plis, K., Bunescu, R., Marling, C., Shubrook, J., & Schwartz, F. (2014). A machine learning approach to predicting blood glucose levels for diabetes management. Twenty-Eighth AAAI Conference on Artificial Intelligence., (págs. 35-39).

Pogorelc , B., Bosnić, Z., & Gams, M. (2011). Automatic recognition of gait-related health problems in the elderly using machine learning. Multimed Tools Appl, 334-354.

Rativa, D., Fernandes, B. J., & Roque, A. (2018). Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions. IEEE journal of translational engineering in health and medicine, 6, 1-9.

Reamaroon, N., Sjoding, M. W., Lin, K., Iwashyna, , T. J., & Najarian, K. (2019). Accounting for label uncertainty in machine learning for detection of acute respiratory distress syndrome. IEEE journal of biomedical and health informatics, 23(1), 407-415.

Rodríguez-Piñero, P. T. , P. T. (2007). SVM para la clasificación de asegurados en el seguro del automóvil. In Empresa global y mercados locales: XXI Congreso Anual AEDEM (pág. 70). Madrid : ESIC.

Rubenfeld,, G. D., Caldwell, E., Peabody, E., Weave, J., Martin, D. P., Neff, M., & Hudson, L. (2005). Incidence and outcomes of acute lung injury. New England Journal of Medicine, 353(16), 685-1693.

Samuel, A. L. (1988). Some Studies in Machine Learning Using the Game of Checkers. II. Recent Progress. In Computer Games, 366-400.

Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., & Arnaout, O. (2018). An introduction and overview of machine learning in neurosurgical care. Acta neurochirurgica, 160(1), 29-30.

Shoeb, A., Carlson, D., Panken, E., & Denison, T. (2009). A micropower support vector machine based seizure detection architecture for embedded medical devices. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology society (págs. 4202-4205). IEEE.

Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge, MA.: MIT Press.

Sweilam, N. H., Tharwat, A. A., & Moniem, A. A. (2010). Support vector machine for diagnosis cancer disease: A comparative study. Egyptian Informatics Journal, 2, 81-92.

Tahir, N. M., & Manap, H. H. (2012). Parkinson Disease Gait Classification based on Machine Learning Approach. Journal of Applied Sciences, 12(2), 180-185.

Tong, S., & Koller, D. (2001). Support vector machine active learning with pplications to text classification. Journal of machine learning research, 2, 45-66.

Urcuqui, C., & Navarro, A. (2016). Machine Learning Classifiers for Android Malware Analysis. IEEE Colombian Conference on Communications and Computing 2016. IEEE.

Valgaev, O., Kupzog, F., & Schmeck, H. (2017). Building power demand forecasting usingK-nearest neighbours model–practical application in Smart City Demo Aspern project. CIRED-Open Access Proceedings Journal, 1, 1601-1604.

Publicado
2019-09-04
Cómo citar
González-Islas , J. C. (2019). Aprendizaje Automático en Aplicaciones Fisioterapéuticas. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 7(Especial), 104-110. https://doi.org/10.29057/icbi.v7iEspecial.4473