Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning
One of the technological aspects that contribute to improving the quality of life of adults, is precisely the enrichment of physical spaces with sensors, video surveillance equipment and actuators, which favor the performance of their daily life activities, which allows discover patterns of human ac...
- Autores:
-
García Restrepo, Johanna Karina
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8179
- Acceso en línea:
- https://hdl.handle.net/11323/8179
https://repositorio.cuc.edu.co/
- Palabra clave:
- Human Activities Recognition (HAR)
Machine learning
Selection techniques
Classification techniques
Activities of Daily Life (ADL)
Dataset
Reconocimiento de actividades humanas
Machine learning
Técnicas de selección
Técnicas de clasificación
Actividades de la vida diaria
Bases de datos
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International
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RCUC2 |
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| dc.title.spa.fl_str_mv |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| title |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| spellingShingle |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning Human Activities Recognition (HAR) Machine learning Selection techniques Classification techniques Activities of Daily Life (ADL) Dataset Reconocimiento de actividades humanas Machine learning Técnicas de selección Técnicas de clasificación Actividades de la vida diaria Bases de datos |
| title_short |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| title_full |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| title_fullStr |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| title_full_unstemmed |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| title_sort |
Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning |
| dc.creator.fl_str_mv |
García Restrepo, Johanna Karina |
| dc.contributor.advisor.spa.fl_str_mv |
De La Hoz Franco, Emiro Ariza Colpas, Paola |
| dc.contributor.author.spa.fl_str_mv |
García Restrepo, Johanna Karina |
| dc.subject.eng.fl_str_mv |
Human Activities Recognition (HAR) Machine learning Selection techniques Classification techniques Activities of Daily Life (ADL) Dataset |
| topic |
Human Activities Recognition (HAR) Machine learning Selection techniques Classification techniques Activities of Daily Life (ADL) Dataset Reconocimiento de actividades humanas Machine learning Técnicas de selección Técnicas de clasificación Actividades de la vida diaria Bases de datos |
| dc.subject.spa.fl_str_mv |
Reconocimiento de actividades humanas Machine learning Técnicas de selección Técnicas de clasificación Actividades de la vida diaria Bases de datos |
| description |
One of the technological aspects that contribute to improving the quality of life of adults, is precisely the enrichment of physical spaces with sensors, video surveillance equipment and actuators, which favor the performance of their daily life activities, which allows discover patterns of human actions generated from the movement and interaction of individuals with the environment, in such a way that they facilitate the monitoring of data and the understanding of the activity of older adults in surveillance environments, based on technology, with the purpose of automatically detecting abnormal patterns, which affect your health or could endanger your life. All these basic activities give older adults the possibility of interacting in community with the tranquility of a personalized and functional medical attention through the implementation of technology. Although the list of activities that a person can perform is extensive, this study focused on those that take place in indoor environments. The recognition of human activities is a field of research that subscribes to an investigative framework, which is the study of activities of daily life. Monitoring the human activities of daily life is a way of describing the functional and health status of a human being. The rapid population growth of older adults has caused an increase in the demand for personal care, particularly for people with affectations typical of senile dementia, due to the correlation that exists between this and the deterioration of memory, intellect, behavior and the consequent decrease in the ability to carry out activities of daily living. Therefore, the need arises to carry out this project, which establishes a predictive model of activities of daily life carried out by inhabitants in indoor environments, through the use of classification and selection techniques based on Machine Learning. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2021-04-22T23:32:28Z |
| dc.date.available.none.fl_str_mv |
2021-04-22T23:32:28Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| status_str |
acceptedVersion |
| dc.identifier.citation.spa.fl_str_mv |
García, J. (2020) Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning. Trabajo de Maestría. Recuperado de https://hdl.handle.net/11323/8179 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/8179 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
García, J. (2020) Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning. Trabajo de Maestría. Recuperado de https://hdl.handle.net/11323/8179 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/8179 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Amiribesheli, M., Benmansour, A., & Bouchachia, A. (2015). A review of smart homes in healthcare. Journal of Ambient Intelligence and Humanized Computing, 6(4), 495–517. https://doi.org/10.1007/s12652-015-0270-2 Andrew McCallum, K. N. (1998). A Comparison of Event Models for Naive Bayes Text Classification. AAAI-98 Workshop on Learning for Text Categorization, 752, 307. https://doi.org/10.3115/1067807.1067848 Asaju, L. B., Shola, P. B., Franklin, N., & Abiola, H. M. (2017). Intrusion Detection System on a Computer Network Using an Ensemble of Randomizable Filtered Classifier, K-Nearest …. Ftstjournal.Com, 2(1), 550–553. www.ftstjournal.com Azevedo, M., & Santos, M. (2005). Data Mining Descoberta de Conhecimento em bBase de Dados. Bidgoli, A., & Naseriparsa, M. (2018). A Hybrid Feature Selection by Resampling, Chi-squared abd Consistency Evaluation Techniques Flexible Interactive Querying View project. In researchgate.net. https://www.researchgate.net/publication/327106211 Björk, K.-M., Eirola, E., Miche, Y., & Lendasse, A. (2016). A new application of machine learning in health care. 1–4. https://doi.org/10.1145/2910674.2935861 Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655 Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. In Classification and Regression Trees. https://doi.org/10.1201/9781315139470 Burgueño, M. J., García-Bastos, J. L., & González-Buitrago, J. M. (1995). ROC curves in the evaluation of diagnostic tests. Medicina Clínica, 104(17), 661–670. Cai, Y. D., Feng, K. Y., Lu, W. C., & Chou, K. C. (2006). Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology, 238(1), 172–176. https://doi.org/10.1016/j.jtbi.2005.05.034 Capela, N. A., Lemaire, E. D., & Baddour, N. (2015). Feature selection for wearable smartphonebased human activity recognition with able bodied, elderly, and stroke patients. PLoS ONE, 10(4), e0124414. https://doi.org/10.1371/journal.pone.0124414 Cardoso, H. L., & Moreira, J. M. (2016). Human Activity Recognition by Means of Online Semisupervised Learning. 2016 17th IEEE International Conference on Mobile Data Management (MDM), 75–77. https://doi.org/10.1109/MDM.2016.93 Carlos, A., D’negri, E., De Vito, E. L., & Zadeh, L. A. (2006). Introducción al razonamiento aproximado: lógica difusa. In Revista Argentina de Medicina Respiratoria Año (Vol. 6). Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. ACM International Conference Proceeding Series, 148, 161–168. https://doi.org/10.1145/1143844.1143865 Catley, C., Smith, K., McGregor, C., & Tracy, M. (2009). Extending CRISP-DM to incorporate temporal data mining of multidimensional medical data streams: A neonatal intensive care unit case study. Proceedings - IEEE Symposium on Computer-Based Medical Systems. https://doi.org/10.1109/CBMS.2009.5255394 Chandra, S., & Maheshkar, S. (2017). Verification of static signature pattern based on random subspace, REP tree and bagging. Multimedia Tools and Applications, 76(18), 19139–19171. https://doi.org/10.1007/s11042-017-4531-2 Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. In Journal of Artificial Intelligence Research (Vol. 16). https://sci-hub.si/http://www.jair.org/index.php/jair/article/view/10302 Chen, L., Nugent, C. D., & Wang, H. (2012). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering, 24(6), 961–974. https://doi.org/10.1109/TKDE.2011.51 Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160. https://doi.org/10.1016/j.catena.2016.11.032 Chen, Y., & Shen, C. (2017). Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access, 5, 3095–3110. https://doi.org/10.1109/ACCESS.2017.2676168 Choudhury, S., & Bhowal, A. (2015). Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, 89–95. https://doi.org/10.1109/ICSTM.2015.7225395 Cook, D. J. (2006). Health monitoring and assistance to support aging in place. Journal of Universal Computer Science, 12(1), 15–29. https://doi.org/10.3217/jucs-012-01-0015 Cook, D. J., Crandall, A., Singla, G., & Thomas, B. (2010). Detection of social interaction in smart spaces. Cybernetics and Systems, 41(2), 90–104. https://doi.org/10.1080/01969720903584183 Cortes, C., Vapnik, V., & Saitta, L. (1995). Support-Vector Networks Editor. In Machine Leaming (Vol. 20). Kluwer Academic Publishers. Crandall, A. S. (2011). BEHAVIOMETRICS FOR MULTIPLE RESIDENTS IN A SMART ENVIRONMENT. https://sci-hub.si/http://research.wsulibs.wsu.edu/xmlui/handle/2376/2855 Crandall, A. S., & Cook, D. J. (2013). Behaviometrics for Identifying Smart Home Residents (pp. 55–71). https://doi.org/10.2991/978-94-6239-018-8_4 Crandall, A. S., & Cook, D. J. (2010). Using a Hidden Markov Model for resident identification. Proceedings - 2010 6th International Conference on Intelligent Environments, IE 2010, 74– 79. https://doi.org/10.1109/IE.2010.21 Daelemans, W., Hoste, V., De Meulder, F., & Naudts, B. (2003). Combined optimization of feature selection and algorithm parameters in machine learning of language. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2837, 84–95. https://doi.org/10.1007/978-3-540-39857-8_10 Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340–341, 250–261. https://doi.org/10.1016/j.ins.2016.01.033 Dietterich, T. G. (1997). Machine-Learning Research. In aaai.org. https://scihub.st/https://www.aaai.org/ojs/index.php/aimagazine/article/view/1324 Ding, D., Cooper, R. A., Pasquina, P. F., & Fici-Pasquina, L. (2011). Sensor technology for smart homes. Maturitas, 69(2), 131–136. https://doi.org/10.1016/j.maturitas.2011.03.016 Du, W. S., & Hu, B. Q. (2014). Approximate distribution reducts in inconsistent interval-valued ordered decision tables. Information Sciences, 271, 93–114. https://doi.org/10.1016/j.ins.2014.02.070 Eddy, S. R. (1998). Profile hidden Markov models. Academic.Oup.Com, 144(9), 755–763. https://academic.oup.com/bioinformatics/article-abstract/14/9/755/259550 Envejecimiento y salud. (2018, February 5). https://www.who.int/es/news-room/factsheets/detail/envejecimiento-y-salud Fahad, L. G., Tahir, S. F., & Rajarajan, M. (2015). Feature selection and data balancing for activity recognition in smart homes. IEEE International Conference on Communications, 2015- Septe, 512–517. https://doi.org/10.1109/ICC.2015.7248373 Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–53. https://vvvvw.aaai.org/ojs/index.php/aimagazine/article/view/1230 Fleury, A., Vacher, M., & Noury, N. (2010). SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results. IEEE Transactions on Information Technology in Biomedicine, 14(2), 274–283. https://doi.org/10.1109/TITB.2009.2037317 Gaikwad, N. B., Tiwari, V., Keskar, A., & Shivaprakash, N. C. (2019). Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification. IEEE Access, 7, 26696–26706. https://doi.org/10.1109/ACCESS.2019.2900084 Galván-Tejada, C. E., Galván-Tejada, J. I., Celaya-Padilla, J. M., Delgado-Contreras, J. R., Magallanes-Quintanar, R., Martinez-Fierro, M. L., Garza-Veloz, I., López-Hernández, Y., & Gamboa-Rosales, H. (2016). An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems, 2016, 1–10. https://doi.org/10.1155/2016/1784101 Gudivada, V. N., Ding, J., & Apon, A. (2017). Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations Flow Cytometry of 3-D structure View project Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transf. October, 1–20. https://www.researchgate.net/publication/318432363 Han, S., Cao, Q., & Han, M. (2012). Parameter selection in SVM with RBF kernel function. World Automation Congress Proceedings. https://ezproxy.cuc.edu.co:2065/document/6321759 He, Y., Li, Y., & Yin, C. (2012). Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS). E-Health Telecommunication Systems and Networks, 01(01), 1–5. https://doi.org/10.4236/etsn.2012.11001 Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601 Hoey, J., Pltz, T., Jackson, D., Monk, A., Pham, C., & Olivier, P. (2011). Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive and Mobile Computing, 7(3), 299–318. https://doi.org/10.1016/j.pmcj.2010.11.007 Islam, A. (2018). Android Application Based Smart Home Automation System Using Internet of Things. 2018 3rd International Conference for Convergence in Technology (I2CT), 1–9. https://doi.org/10.1109/I2CT.2018.8529752 Jalal, A., Kamal, S., & Kim, D. (2017). A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems. International Journal of Interactive Multimedia and Artificial Intelligence, 4(4), 54. https://doi.org/10.9781/ijimai.2017.447 Jiang, X., Liu, M., Zheng, D., Zhao, X., Wang, W., & Sun, G. (2017). Enhanced photocatalytic degradation of organic pollutants using high-aspect-ratio Si/ITO/WO3 micropost photoelectrodes. 2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS), 881–884. https://doi.org/10.1109/MEMSYS.2017.7863549 Kalmegh, S. (2015). Analysis of WEKA Data Mining Algorithm REPTree , Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology, 2(2), 438–446. www.ijiset.com Karegowda, A. G., Manjunath, A. S., & Jayaram, M. A. (2010). Comparative Study of Attribute Selection Using Gain Ratio and Correlation Based Feature Selection. International Journal of Information Technology and Knowledge Management, 2(2), 271–277. https://scihub.st/http://csjournals.com/IJITKM/PDF 3-1/19.pdf Khalajzadeh, H., Mansouri, M., & Teshnehlab, M. (2014). Face recognition using convolutional neural network and simple logistic classifier. Advances in Intelligent Systems and Computing, 223, 197–207. https://doi.org/10.1007/978-3-319-00930-8_18 Khalifa, S., Lan, G., Hassan, M., Seneviratne, A., & Das, S. K. (2018). HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices. IEEE Transactions on Mobile Computing, 17(6), 1353–1368. https://doi.org/10.1109/TMC.2017.2761744 Kira, K., & Rendell, L. A. (1992). Feature selection problem: traditional methods and a new algorithm. Proceedings Tenth National Conference on Artificial Intelligence, 129–134. https://sci-hub.st/https://www.aaai.org/Library/AAAI/1992/aaai92-020.php Kumar, K., Kumar, G., & Kumar, Y. (2013). Feature Selection Approach for Intrusion Detection System. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2(5), 47–53. http://warse.org/pdfs/2013/icceitsp09.pdf Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59(1–2), 161–205. https://doi.org/10.1007/s10994-005-0466-3 Lladó, M. R., Código, H., Lennin, A., Quiroz, P., & Lima -Perú, V. (2020). ENTORNO DOMÓTICO ADAPTADO A PERSONAS CON DISCAPACIDAD FÍSICA UTILIZANDO MODELOS OCULTOS DE MARKOV Tesis para optar el Título Profesional de Ingeniero de Sistemas. In Repositorio Institucional - Ulima. Universidad de Lima. http://repositorio.ulima.edu.pe/handle/20.500.12724/11664 López de Ullibarri, G. I., & Píta Fernández, S. (1998). Curvas ROC. Cad Aten Primaria, 5(4), 229– 235. López Saca, F., Ferreyra Ramírez, A., Avilés Cruz, C., Villegas Cortez, J., Zúñiga López, A., & Rodrigez Martinez, E. (2018). Preprocesamiento de bases de datos de imágenes para mejorar el rendimiento de redes neuronales convolucionales. Research in Computing Science, 147(7), 35–45. https://doi.org/10.13053/rcs-147-7-3 Marcondes, C. H., & Almeida Campos, M. L. de. (2008). ONTOLOGIA E WEB SEMÂNTICA: O ESPAÇO DA PESQUISA EM CIÊNCIA DA INFORMAÇÃO. PontodeAcesso, 2(1), 107. https://doi.org/10.9771/1981-6766rpa.v2i1.2669 Mejia-Ricart, L. F., Helling, P., & Olmsted, A. (2018). Evaluate action primitives for human activity recognition using unsupervised learning approach. 2017 12th International Conference for Internet Technology and Secured Transactions, ICITST 2017, 186–188. https://doi.org/10.23919/ICITST.2017.8356374 Mishra, A. (2018). Metrics to Evaluate your Machine Learning Algorithm. Murata, N., Yoshizawa, S., & Amari, S. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks, 5(6), 865–872. https://doi.org/10.1109/72.329683 Nagalla, R., Pothuganti, P., & Pawar, D. S. (2017). Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests. Procedia Computer Science, 109, 474–481. https://doi.org/10.1016/j.procs.2017.05.312 Nagi, S. Z., Burk, R. D., & Potter, H. R. (1965). Back disorders and rehabilitation achievement. Journal of Chronic Diseases, 18(2), 181–197. https://doi.org/10.1016/0021-9681(65)90101- 3 Nettleton, D. F., Orriols-Puig, A., & Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review, 33(4), 275–306. https://doi.org/10.1007/s10462-010-9156-z Nuraeni, F., Febriani Sm, N. N., Listiani, L., & Rahmawati, E. (2019). Implementation of K-Means Algorithm with Distance of Euclidean Proximity in Clustering Cases of Violence Against Women and Children. 2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019, 162–167. https://doi.org/10.1109/ICORIS.2019.8874883 Parimala, R., & Nallaswamy, R. (2011). A Study of Spam E-mail classification using Feature Selection package. Global Journal of Computer Science and Technology, 11(7), 45–54. https://computerresearch.org/index.php/computer/article/view/727 Qian, H., Mao, Y., Xiang, W., & Wang, Z. (2010). Recognition of human activities using SVM multi-class classifier. Pattern Recognition Letters, 31(2), 100–111. https://doi.org/10.1016/j.patrec.2009.09.019 Rajput, A., Aharwal, R. P., Dubey, M., Saxena, S. P., & Raghuvanshi, M. (2011). J48 and JRIP rules for e-governance data. International Journal of Computer Science and Security, 5(2), 201–207. Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, 1, 10–17. https://doi.org/10.1109/iccv.2003.1238308 Ricardo, S., Pereira Isabel Hernández Arteaga Segundo, T., Caicedo Zambrano Arsenio, J., Troya, H., & Carlos Alvarado Pérez, J. (2015). DESCUBRIMIENTO DE PATRONES DE DESEMPEÑO ACADÉMICO. https://scihub.st/https://repository.ucc.edu.co/handle/20.500.12494/1039 Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244. https://doi.org/10.1016/j.eswa.2016.04.032 Ruan, Y. X., Lin, H. T., & Tsai, M. F. (2014). Improving ranking performance with cost-sensitive ordinal classification via regression. Information Retrieval, 17(1), 1–20. https://doi.org/10.1007/s10791-013-9219-2 Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3), 235–240. https://doi.org/10.1007/bf00993309 Seelye, A., Schmitter-Edgecombe, M., Cook, D. J., & Crandall, A. (2013). Smart environment prompting technologies for everyday activities in mild cognitive impairment. Journal of the International Neuropsychological Society, 19, 442–452. Shah, C. (2020). Supervised Learning. In A Hands-On Introduction to Data Science (pp. 235–289). https://doi.org/10.1017/9781108560412.010 Spruit, M., Vroon, R., & Batenburg, R. (2014). Towards healthcare business intelligence in longterm care: An explorative case study in the Netherlands. Computers in Human Behavior, 30, 698–707. https://doi.org/10.1016/j.chb.2013.07.038 Suykens, J. A. K., & Vandewalle, J. (1999). Training multilayer perceptron classifiers based on a modified support vector method. IEEE Transactions on Neural Networks, 10(4), 907–911. https://doi.org/10.1109/72.774254 Tabuenca Dopico, P., Sánchez Espeso, P. P., & Villar Bonet, E. (1993). Realización de un planificador inteligente para síntesis de alto nivel. VIII Congreso Diseño de Circuitos Integrados: Málaga, 9 Al 11 de Noviembre de 1993, 1993, Págs. 315-319, 315–319. https://dialnet.unirioja.es/servlet/articulo?codigo=6418065&info=resumen&idioma=SPA Universidad Pedagógica y Tecnológica de Colombia. (2004). Unidad 1 Estadistica Descriptiva. Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064 Yiyu, Y. (2007). Decision-theoretic rough set models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4481 LNAI, 1–12. https://doi.org/10.1007/978-3-540-72458-2_1 Yuan, G., Wang, Z., Meng, F., Yan, Q., & Xia, S. (2019). An overview of human activity recognition based on smartphone. Sensor Review, 39(2), 288–306. https://doi.org/10.1108/SR-11-2017-0245 |
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De La Hoz Franco, EmiroAriza Colpas, PaolaGarcía Restrepo, Johanna Karina2021-04-22T23:32:28Z2021-04-22T23:32:28Z2020García, J. (2020) Modelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning. Trabajo de Maestría. Recuperado de https://hdl.handle.net/11323/8179https://hdl.handle.net/11323/8179Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/One of the technological aspects that contribute to improving the quality of life of adults, is precisely the enrichment of physical spaces with sensors, video surveillance equipment and actuators, which favor the performance of their daily life activities, which allows discover patterns of human actions generated from the movement and interaction of individuals with the environment, in such a way that they facilitate the monitoring of data and the understanding of the activity of older adults in surveillance environments, based on technology, with the purpose of automatically detecting abnormal patterns, which affect your health or could endanger your life. All these basic activities give older adults the possibility of interacting in community with the tranquility of a personalized and functional medical attention through the implementation of technology. Although the list of activities that a person can perform is extensive, this study focused on those that take place in indoor environments. The recognition of human activities is a field of research that subscribes to an investigative framework, which is the study of activities of daily life. Monitoring the human activities of daily life is a way of describing the functional and health status of a human being. The rapid population growth of older adults has caused an increase in the demand for personal care, particularly for people with affectations typical of senile dementia, due to the correlation that exists between this and the deterioration of memory, intellect, behavior and the consequent decrease in the ability to carry out activities of daily living. Therefore, the need arises to carry out this project, which establishes a predictive model of activities of daily life carried out by inhabitants in indoor environments, through the use of classification and selection techniques based on Machine Learning.Uno de los aspectos tenológicos que contribuyen a mejorar la calidad de vida de los adultos, es precisamente, el enriquecimiento de espacios físicos con sensores, equipos de video vigilancia y actuadores, que favorezcen la realización de sus actividades de la vida diaria, lo que permite descubrir patrones de acciones humanas generados a partir del movimiento y la interacción de los individuos con el ambiente, de tal manera que faciliten el monitoreo de datos y la comprensión de la actividad de los adultos mayores en entornos de vigilancia, basados en tecnología, con el propósito de detectar automáticamente patrones anormales, que afecten su salud o puedan poner en riesgo su vida. Todas estas actividades básicas les confieren a los adultos mayores la posibilidad de interactuar en comunidad con la tranquilidad de una atención médica personalizada y funcional a través de la implementación de tecnología. Aunque la lista de actividades que puede realizar una persona es extensa, este estudio se enfocó en aquellas que se desarrollan en ambientes indoor. El reconocimiento de actividades humanas es un ámbito de investigación que se suscribe a un marco investigativo, que es el estudio de las actividades de la vida diaria. Monitorear las actividades humanas de la vida diaria es una forma de describir el estado funcional y de salud de un ser humano. El rápido crecimiento poblacional de adultos mayores ha provocado un aumento en la demanda del cuidado personal, particularmente para personas con afectaciones propias de la demencia senil, debido a la correlación que existe entre esta y el deterioro de la memoria, el intelecto, el comportamiento y la consecuente disminución de la capacidad para realizar actividades de la vida diaria. Por tanto, surge la necesidad de realizar este proyecto, que establece un modelo predictivo de actividades de la vida diaria realizadas por habitantes en ambientes indoor, mediante el uso de técnicas de clasificación y selección basadas en Machine Learning.García Restrepo, Johanna Karinaapplication/pdfspaCorporación Universidad de la CostaMaestría en Gestión de las Tecnologías de Información y la ComunicaciónAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Human Activities Recognition (HAR)Machine learningSelection techniquesClassification techniquesActivities of Daily Life (ADL)DatasetReconocimiento de actividades humanasMachine learningTécnicas de selecciónTécnicas de clasificaciónActividades de la vida diariaBases de datosModelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine LearningTrabajo de grado - MaestríaTextinfo:eu-repo/semantics/masterThesishttp://purl.org/redcol/resource_type/TMinfo:eu-repo/semantics/acceptedVersionAmiribesheli, M., Benmansour, A., & Bouchachia, A. (2015). A review of smart homes in healthcare. Journal of Ambient Intelligence and Humanized Computing, 6(4), 495–517. https://doi.org/10.1007/s12652-015-0270-2Andrew McCallum, K. N. (1998). A Comparison of Event Models for Naive Bayes Text Classification. AAAI-98 Workshop on Learning for Text Categorization, 752, 307. https://doi.org/10.3115/1067807.1067848Asaju, L. B., Shola, P. B., Franklin, N., & Abiola, H. M. (2017). Intrusion Detection System on a Computer Network Using an Ensemble of Randomizable Filtered Classifier, K-Nearest …. Ftstjournal.Com, 2(1), 550–553. www.ftstjournal.comAzevedo, M., & Santos, M. (2005). Data Mining Descoberta de Conhecimento em bBase de Dados.Bidgoli, A., & Naseriparsa, M. (2018). A Hybrid Feature Selection by Resampling, Chi-squared abd Consistency Evaluation Techniques Flexible Interactive Querying View project. In researchgate.net. https://www.researchgate.net/publication/327106211Björk, K.-M., Eirola, E., Miche, Y., & Lendasse, A. (2016). A new application of machine learning in health care. 1–4. https://doi.org/10.1145/2910674.2935861Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. In Classification and Regression Trees. https://doi.org/10.1201/9781315139470Burgueño, M. J., García-Bastos, J. L., & González-Buitrago, J. M. (1995). ROC curves in the evaluation of diagnostic tests. Medicina Clínica, 104(17), 661–670.Cai, Y. D., Feng, K. Y., Lu, W. C., & Chou, K. C. (2006). Using LogitBoost classifier to predict protein structural classes. Journal of Theoretical Biology, 238(1), 172–176. https://doi.org/10.1016/j.jtbi.2005.05.034Capela, N. A., Lemaire, E. D., & Baddour, N. (2015). Feature selection for wearable smartphonebased human activity recognition with able bodied, elderly, and stroke patients. PLoS ONE, 10(4), e0124414. https://doi.org/10.1371/journal.pone.0124414Cardoso, H. L., & Moreira, J. M. (2016). Human Activity Recognition by Means of Online Semisupervised Learning. 2016 17th IEEE International Conference on Mobile Data Management (MDM), 75–77. https://doi.org/10.1109/MDM.2016.93Carlos, A., D’negri, E., De Vito, E. L., & Zadeh, L. A. (2006). Introducción al razonamiento aproximado: lógica difusa. In Revista Argentina de Medicina Respiratoria Año (Vol. 6).Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. ACM International Conference Proceeding Series, 148, 161–168. https://doi.org/10.1145/1143844.1143865Catley, C., Smith, K., McGregor, C., & Tracy, M. (2009). Extending CRISP-DM to incorporate temporal data mining of multidimensional medical data streams: A neonatal intensive care unit case study. Proceedings - IEEE Symposium on Computer-Based Medical Systems. https://doi.org/10.1109/CBMS.2009.5255394Chandra, S., & Maheshkar, S. (2017). Verification of static signature pattern based on random subspace, REP tree and bagging. Multimedia Tools and Applications, 76(18), 19139–19171. https://doi.org/10.1007/s11042-017-4531-2Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. In Journal of Artificial Intelligence Research (Vol. 16). https://sci-hub.si/http://www.jair.org/index.php/jair/article/view/10302Chen, L., Nugent, C. D., & Wang, H. (2012). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering, 24(6), 961–974. https://doi.org/10.1109/TKDE.2011.51Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160. https://doi.org/10.1016/j.catena.2016.11.032Chen, Y., & Shen, C. (2017). Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition. IEEE Access, 5, 3095–3110. https://doi.org/10.1109/ACCESS.2017.2676168Choudhury, S., & Bhowal, A. (2015). Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, 89–95. https://doi.org/10.1109/ICSTM.2015.7225395Cook, D. J. (2006). Health monitoring and assistance to support aging in place. Journal of Universal Computer Science, 12(1), 15–29. https://doi.org/10.3217/jucs-012-01-0015Cook, D. J., Crandall, A., Singla, G., & Thomas, B. (2010). Detection of social interaction in smart spaces. Cybernetics and Systems, 41(2), 90–104. https://doi.org/10.1080/01969720903584183Cortes, C., Vapnik, V., & Saitta, L. (1995). Support-Vector Networks Editor. In Machine Leaming (Vol. 20). Kluwer Academic Publishers.Crandall, A. S. (2011). BEHAVIOMETRICS FOR MULTIPLE RESIDENTS IN A SMART ENVIRONMENT. https://sci-hub.si/http://research.wsulibs.wsu.edu/xmlui/handle/2376/2855Crandall, A. S., & Cook, D. J. (2013). Behaviometrics for Identifying Smart Home Residents (pp. 55–71). https://doi.org/10.2991/978-94-6239-018-8_4Crandall, A. S., & Cook, D. J. (2010). Using a Hidden Markov Model for resident identification. Proceedings - 2010 6th International Conference on Intelligent Environments, IE 2010, 74– 79. https://doi.org/10.1109/IE.2010.21Daelemans, W., Hoste, V., De Meulder, F., & Naudts, B. (2003). Combined optimization of feature selection and algorithm parameters in machine learning of language. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2837, 84–95. https://doi.org/10.1007/978-3-540-39857-8_10Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340–341, 250–261. https://doi.org/10.1016/j.ins.2016.01.033Dietterich, T. G. (1997). Machine-Learning Research. In aaai.org. https://scihub.st/https://www.aaai.org/ojs/index.php/aimagazine/article/view/1324Ding, D., Cooper, R. A., Pasquina, P. F., & Fici-Pasquina, L. (2011). Sensor technology for smart homes. Maturitas, 69(2), 131–136. https://doi.org/10.1016/j.maturitas.2011.03.016Du, W. S., & Hu, B. Q. (2014). Approximate distribution reducts in inconsistent interval-valued ordered decision tables. Information Sciences, 271, 93–114. https://doi.org/10.1016/j.ins.2014.02.070Eddy, S. R. (1998). Profile hidden Markov models. Academic.Oup.Com, 144(9), 755–763. https://academic.oup.com/bioinformatics/article-abstract/14/9/755/259550Envejecimiento y salud. (2018, February 5). https://www.who.int/es/news-room/factsheets/detail/envejecimiento-y-saludFahad, L. G., Tahir, S. F., & Rajarajan, M. (2015). Feature selection and data balancing for activity recognition in smart homes. IEEE International Conference on Communications, 2015- Septe, 512–517. https://doi.org/10.1109/ICC.2015.7248373Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–53. https://vvvvw.aaai.org/ojs/index.php/aimagazine/article/view/1230Fleury, A., Vacher, M., & Noury, N. (2010). SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results. IEEE Transactions on Information Technology in Biomedicine, 14(2), 274–283. https://doi.org/10.1109/TITB.2009.2037317Gaikwad, N. B., Tiwari, V., Keskar, A., & Shivaprakash, N. C. (2019). Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification. IEEE Access, 7, 26696–26706. https://doi.org/10.1109/ACCESS.2019.2900084Galván-Tejada, C. E., Galván-Tejada, J. I., Celaya-Padilla, J. M., Delgado-Contreras, J. R., Magallanes-Quintanar, R., Martinez-Fierro, M. L., Garza-Veloz, I., López-Hernández, Y., & Gamboa-Rosales, H. (2016). An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems, 2016, 1–10. https://doi.org/10.1155/2016/1784101Gudivada, V. N., Ding, J., & Apon, A. (2017). Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations Flow Cytometry of 3-D structure View project Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transf. October, 1–20. https://www.researchgate.net/publication/318432363Han, S., Cao, Q., & Han, M. (2012). Parameter selection in SVM with RBF kernel function. World Automation Congress Proceedings. https://ezproxy.cuc.edu.co:2065/document/6321759He, Y., Li, Y., & Yin, C. (2012). Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS). E-Health Telecommunication Systems and Networks, 01(01), 1–5. https://doi.org/10.4236/etsn.2012.11001Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601Hoey, J., Pltz, T., Jackson, D., Monk, A., Pham, C., & Olivier, P. (2011). Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive and Mobile Computing, 7(3), 299–318. https://doi.org/10.1016/j.pmcj.2010.11.007Islam, A. (2018). Android Application Based Smart Home Automation System Using Internet of Things. 2018 3rd International Conference for Convergence in Technology (I2CT), 1–9. https://doi.org/10.1109/I2CT.2018.8529752Jalal, A., Kamal, S., & Kim, D. (2017). A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems. International Journal of Interactive Multimedia and Artificial Intelligence, 4(4), 54. https://doi.org/10.9781/ijimai.2017.447Jiang, X., Liu, M., Zheng, D., Zhao, X., Wang, W., & Sun, G. (2017). Enhanced photocatalytic degradation of organic pollutants using high-aspect-ratio Si/ITO/WO3 micropost photoelectrodes. 2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS), 881–884. https://doi.org/10.1109/MEMSYS.2017.7863549Kalmegh, S. (2015). Analysis of WEKA Data Mining Algorithm REPTree , Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology, 2(2), 438–446. www.ijiset.comKaregowda, A. G., Manjunath, A. S., & Jayaram, M. A. (2010). Comparative Study of Attribute Selection Using Gain Ratio and Correlation Based Feature Selection. International Journal of Information Technology and Knowledge Management, 2(2), 271–277. https://scihub.st/http://csjournals.com/IJITKM/PDF 3-1/19.pdfKhalajzadeh, H., Mansouri, M., & Teshnehlab, M. (2014). Face recognition using convolutional neural network and simple logistic classifier. Advances in Intelligent Systems and Computing, 223, 197–207. https://doi.org/10.1007/978-3-319-00930-8_18Khalifa, S., Lan, G., Hassan, M., Seneviratne, A., & Das, S. K. (2018). HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices. IEEE Transactions on Mobile Computing, 17(6), 1353–1368. https://doi.org/10.1109/TMC.2017.2761744Kira, K., & Rendell, L. A. (1992). Feature selection problem: traditional methods and a new algorithm. Proceedings Tenth National Conference on Artificial Intelligence, 129–134. https://sci-hub.st/https://www.aaai.org/Library/AAAI/1992/aaai92-020.phpKumar, K., Kumar, G., & Kumar, Y. (2013). Feature Selection Approach for Intrusion Detection System. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2(5), 47–53. http://warse.org/pdfs/2013/icceitsp09.pdfLandwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59(1–2), 161–205. https://doi.org/10.1007/s10994-005-0466-3Lladó, M. R., Código, H., Lennin, A., Quiroz, P., & Lima -Perú, V. (2020). ENTORNO DOMÓTICO ADAPTADO A PERSONAS CON DISCAPACIDAD FÍSICA UTILIZANDO MODELOS OCULTOS DE MARKOV Tesis para optar el Título Profesional de Ingeniero de Sistemas. In Repositorio Institucional - Ulima. Universidad de Lima. http://repositorio.ulima.edu.pe/handle/20.500.12724/11664López de Ullibarri, G. I., & Píta Fernández, S. (1998). Curvas ROC. Cad Aten Primaria, 5(4), 229– 235.López Saca, F., Ferreyra Ramírez, A., Avilés Cruz, C., Villegas Cortez, J., Zúñiga López, A., & Rodrigez Martinez, E. (2018). Preprocesamiento de bases de datos de imágenes para mejorar el rendimiento de redes neuronales convolucionales. Research in Computing Science, 147(7), 35–45. https://doi.org/10.13053/rcs-147-7-3Marcondes, C. H., & Almeida Campos, M. L. de. (2008). ONTOLOGIA E WEB SEMÂNTICA: O ESPAÇO DA PESQUISA EM CIÊNCIA DA INFORMAÇÃO. PontodeAcesso, 2(1), 107. https://doi.org/10.9771/1981-6766rpa.v2i1.2669Mejia-Ricart, L. F., Helling, P., & Olmsted, A. (2018). Evaluate action primitives for human activity recognition using unsupervised learning approach. 2017 12th International Conference for Internet Technology and Secured Transactions, ICITST 2017, 186–188. https://doi.org/10.23919/ICITST.2017.8356374Mishra, A. (2018). Metrics to Evaluate your Machine Learning Algorithm.Murata, N., Yoshizawa, S., & Amari, S. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks, 5(6), 865–872. https://doi.org/10.1109/72.329683Nagalla, R., Pothuganti, P., & Pawar, D. S. (2017). Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests. Procedia Computer Science, 109, 474–481. https://doi.org/10.1016/j.procs.2017.05.312Nagi, S. Z., Burk, R. D., & Potter, H. R. (1965). Back disorders and rehabilitation achievement. Journal of Chronic Diseases, 18(2), 181–197. https://doi.org/10.1016/0021-9681(65)90101- 3Nettleton, D. F., Orriols-Puig, A., & Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial Intelligence Review, 33(4), 275–306. https://doi.org/10.1007/s10462-010-9156-zNuraeni, F., Febriani Sm, N. N., Listiani, L., & Rahmawati, E. (2019). Implementation of K-Means Algorithm with Distance of Euclidean Proximity in Clustering Cases of Violence Against Women and Children. 2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019, 162–167. https://doi.org/10.1109/ICORIS.2019.8874883Parimala, R., & Nallaswamy, R. (2011). A Study of Spam E-mail classification using Feature Selection package. Global Journal of Computer Science and Technology, 11(7), 45–54. https://computerresearch.org/index.php/computer/article/view/727Qian, H., Mao, Y., Xiang, W., & Wang, Z. (2010). Recognition of human activities using SVM multi-class classifier. Pattern Recognition Letters, 31(2), 100–111. https://doi.org/10.1016/j.patrec.2009.09.019Rajput, A., Aharwal, R. P., Dubey, M., Saxena, S. P., & Raghuvanshi, M. (2011). J48 and JRIP rules for e-governance data. International Journal of Computer Science and Security, 5(2), 201–207.Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, 1, 10–17. https://doi.org/10.1109/iccv.2003.1238308Ricardo, S., Pereira Isabel Hernández Arteaga Segundo, T., Caicedo Zambrano Arsenio, J., Troya, H., & Carlos Alvarado Pérez, J. (2015). DESCUBRIMIENTO DE PATRONES DE DESEMPEÑO ACADÉMICO. https://scihub.st/https://repository.ucc.edu.co/handle/20.500.12494/1039Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244. https://doi.org/10.1016/j.eswa.2016.04.032Ruan, Y. X., Lin, H. T., & Tsai, M. F. (2014). Improving ranking performance with cost-sensitive ordinal classification via regression. Information Retrieval, 17(1), 1–20. https://doi.org/10.1007/s10791-013-9219-2Salzberg, S. L. (1994). C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Machine Learning, 16(3), 235–240. https://doi.org/10.1007/bf00993309Seelye, A., Schmitter-Edgecombe, M., Cook, D. J., & Crandall, A. (2013). Smart environment prompting technologies for everyday activities in mild cognitive impairment. Journal of the International Neuropsychological Society, 19, 442–452.Shah, C. (2020). Supervised Learning. In A Hands-On Introduction to Data Science (pp. 235–289). https://doi.org/10.1017/9781108560412.010Spruit, M., Vroon, R., & Batenburg, R. (2014). Towards healthcare business intelligence in longterm care: An explorative case study in the Netherlands. Computers in Human Behavior, 30, 698–707. https://doi.org/10.1016/j.chb.2013.07.038Suykens, J. A. K., & Vandewalle, J. (1999). Training multilayer perceptron classifiers based on a modified support vector method. IEEE Transactions on Neural Networks, 10(4), 907–911. https://doi.org/10.1109/72.774254Tabuenca Dopico, P., Sánchez Espeso, P. P., & Villar Bonet, E. (1993). Realización de un planificador inteligente para síntesis de alto nivel. VIII Congreso Diseño de Circuitos Integrados: Málaga, 9 Al 11 de Noviembre de 1993, 1993, Págs. 315-319, 315–319. https://dialnet.unirioja.es/servlet/articulo?codigo=6418065&info=resumen&idioma=SPAUniversidad Pedagógica y Tecnológica de Colombia. (2004). Unidad 1 Estadistica Descriptiva.Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064Yiyu, Y. (2007). Decision-theoretic rough set models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4481 LNAI, 1–12. https://doi.org/10.1007/978-3-540-72458-2_1Yuan, G., Wang, Z., Meng, F., Yan, Q., & Xia, S. (2019). An overview of human activity recognition based on smartphone. Sensor Review, 39(2), 288–306. https://doi.org/10.1108/SR-11-2017-0245PublicationORIGINALModelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning.pdfModelo predictivo para la identificación de actividades de la vida diaria (ADL) en ambientes INDOOR usando técnicas de clasificación basadas en machine Learning.pdfapplication/pdf1532509https://repositorio.cuc.edu.co/bitstreams/1cff57f9-24cf-43ed-a471-4a93871dfb48/download0d2831b170b3c95241e439224f1e624dMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81031https://repositorio.cuc.edu.co/bitstreams/1bddce09-7f67-4b37-af3a-224febb24b71/download934f4ca17e109e0a05eaeaba504d7ce4MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/008adcd3-c9de-474c-ba47-e1d00d58a9a8/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILModelo predictivo para la identificación de actividades 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