Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering

Human Activity Recognition (HAR) is a topic of great relevance due to its wide range of applications, with various approaches being proposed to recognize these activities, from comparing signals with thresholds to applying machine learning and deep learning techniques. The development of computation...

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Autores:: Pacheco Cuentas, Rosberg Yaser

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: spa

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network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
title	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
spellingShingle	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering Human activity recognition HAR Daily life activities ADL Classification methods Smart home Clustering Ensemble methods Reconocimiento de actividades humanas Actividades de la vida diaria Métodos de clasificación Hogar inteligente Clustering Métodos ensamblados
title_short	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
title_full	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
title_fullStr	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
title_full_unstemmed	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
title_sort	Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering
dc.creator.fl_str_mv	Pacheco Cuentas, Rosberg Yaser
dc.contributor.advisor.none.fl_str_mv	Morales Otega Roberto Ariza Colpas Paola
dc.contributor.author.none.fl_str_mv	Pacheco Cuentas, Rosberg Yaser
dc.contributor.jury.none.fl_str_mv	Melendez Pertuz Farid Patiño Saucedo Janns Diaz Martinez Jorge
dc.subject.proposal.eng.fl_str_mv	Human activity recognition HAR Daily life activities ADL Classification methods Smart home Clustering Ensemble methods
topic	Human activity recognition HAR Daily life activities ADL Classification methods Smart home Clustering Ensemble methods Reconocimiento de actividades humanas Actividades de la vida diaria Métodos de clasificación Hogar inteligente Clustering Métodos ensamblados
dc.subject.proposal.spa.fl_str_mv	Reconocimiento de actividades humanas Actividades de la vida diaria Métodos de clasificación Hogar inteligente Clustering Métodos ensamblados
description	Human Activity Recognition (HAR) is a topic of great relevance due to its wide range of applications, with various approaches being proposed to recognize these activities, from comparing signals with thresholds to applying machine learning and deep learning techniques. The development of computational systems capable of performing this recognition and extracting truthful, useful, compact, and natural language-like information is a very active area of knowledge and encompasses a research field that subscribes to an investigative framework, which is the study of daily life activities (ADL), where efforts from researchers in different areas of knowledge come together. This frames the present research and its fundamental objective is to advance in the development of a model that allows solving the problem of human activity recognition through the automatic analysis of datasets based on unsupervised learning techniques. The research required the execution of a series of phases: characterization, experimentation, and evaluation. During the characterization phase, a public human activity recognition dataset, CASAS Kyoto, was selected, which is stored in databases that pre-trained models use to generate useful knowledge, from analyzing patterns, generating predictions, and identifying behavior trends. During the experimentation phase, an ensemble-based model was applied, which utilized the advantages of both supervised and unsupervised methods to consolidate results capable of supporting a closer identification of these activities
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-10-23T16:32:07Z
dc.date.available.none.fl_str_mv	2024-10-23T16:32:07Z
dc.date.issued.none.fl_str_mv	2024
dc.type.none.fl_str_mv	Trabajo de grado - Maestría
dc.type.content.none.fl_str_mv	Text
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.redcol.none.fl_str_mv	http://purl.org/redcol/resource_type/TM
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/11323/13501
dc.identifier.instname.none.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.none.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.none.fl_str_mv	https://repositorio.cuc.edu.co/
url	https://hdl.handle.net/11323/13501 https://repositorio.cuc.edu.co/
identifier_str_mv	Corporación Universidad de la Costa REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv	spa
language	spa
dc.relation.references.none.fl_str_mv	Organización Panamericana de la Salud (OPS). (2023, octubre). Demencia. Available online: https://www.paho.org/es/temas/demencia Sikder, N., & Nahid, A.-A. (2021). KU-HAR: An open dataset for heterogeneous human activity recognition. Pattern Recognition Letters, 146, 46–54. Popescu, A.-C., Mocanu, I., & Cramariuc, B. (2019). PRECIS HAR. Retrieved from https://ieee dataport.org/open-access/precishar (accessed on 30 October Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort Asturiano, C. (2019). UP-Fall Detection Dataset: A multimodal approach. Sensors, 19(1988). Van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea, 21–24 September 2008; pp. 1–9. Singla, G.; Cook, D.; Schmitter-Edgecombe, M. Recognizing independent and joint activities among multiple residents in smart environments. Ambient. Intell. Humaniz. Comput. J. 2010, 1, 57–63. Weiss, G.M.; Yoneda, K.; Hayajneh, T. Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living. IEEE Access 2019, 7, 133190–133202. Gallissot, M.; Caelen, J.; Bonnefond, N.; Meillon, B.; Pons, S. Using the Multicom Domus Dataset; Research Report RR-LIG-020; LIG: Grenoble, France, 2011 Roggen, D.; Calatroni, A.; Rossi, M.; Holleczek, T.; Tröster, G.; Lukowicz, P.; Pirkl, G.; Bannach, D.; Ferscha, A.; Doppler, J.; et al. Collecting complex activity data sets in highly rich networked sensor environments. In Proceedings of the Seventh International Conference on Networked Sensing Systems (INSS’10), Kassel, Germany, 15–18 June 2010. Cook, D. Learning setting-generalized activity mdoels for smart spaces. IEEE Intell. Syst. 2010, 1. Zhang, M.; Sawchuk, A.A. USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. In Proceedings of the ACM International Conference on Ubiquitous Computing (UbiComp) Workshop on Situation, Activity and Goal Awareness (SAGAware), Pittsburgh, PA, USA, 5–8 September 2012. Logan, B.; Healey, B.J.; Philipose, J.M.; Tapia, E.M.; Intille, S. A long-term evaluation of sensing modalities for activity recognition. In Proceedings of the International Conference on Ubiquitous Computing, Taipei, Taiwan, 17–20 December 2007; Springer: Berlin/Heidelberg, Germany, 2007; pp. 483–500 Nugent, C.D.; Mulvenna, M.D.; Hong, X.; Devlin, S. Experiences in the development of a Smart Lab. Int. J. Biomed. Eng. Technol. 2009, 2, 319–331. Schmitter-Edgecombe, M.; Cook, D.J. Assessing the Quality of Activities in a Smart Environment. Methods Inf. Med. 2009, 48, 480–485. Reiss, A.; Stricker, D. Introducing a New Benchmarked Dataset for Activity Monitoring. In Proceedings of the 16th IEEE International Symposium on Wearable Computers (ISWC), Newcastle, UK, 18–22 June 2012. Banos, O.; Garcia, R.; Holgado, J.A.; Damas, M.; Pomares, H.; Rojas, I.; Saez, A.; Villalonga, C. mHealthDroid: A novel framework for agile development of mobile health applications. In Proceedings of the 6th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2014), Belfast, UK, 2–5 December 2014. Barshan, B.; Yüksek, M.C. Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput. J. 2014, 57, 1649–1667 Espinilla, M.; Martínez, L.; Medina, J.; Nugent, C. The experience of developing theUJAmI Smart lab. IEEE Access. 2018, 6, 34631–34642. Y. Chen and C. Shen, “Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition,” IEEE Access, vol. 5, pp. 3095–3110, 2017, DOI: 10.1109/ACCESS.2017.2676168. C. A. Ronao and S. B. Cho, “Human activity recognition with smartphone sensors using deep learning neural networks,” Expert Systems with Applications, vol. 59, pp. 235–244, Oct. 2016, DOI: 10.1016/j.eswa.2016.04.032. N. A. Capela, E. D. Lemaire, and N. Baddour, “Feature selection for wearable smartphone-based human activity recog-nition with able bodied, elderly, and stroke patients,” PLoS ONE, vol. 10, no. 4, p. e0124414, Apr. 2015, DOI: 10.1371/journal.pone.0124414. V. N. Gudivada, J. Ding, and A. Apon, “Data Quality Considerations for Big Data and Machine Learning: Going Be-yond 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,” no. October, pp. 1–20, 2017, Accessed: Sep. 11, 2022. [Online]. Available: https://www.researchgate.net/publication/318432363 Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27. Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106. Kohavi, R., & Quinlan, J. R. (2002). Data mining tasks and methods: Classification: decision-tree discovery. In Handbook of data mining and knowledge discovery (pp. 267-276). Vapnik, V. (1998). The support vector method of function estimation. Nonlinear modeling: Advanced black-box techniques, 55-85. Castelli, V. 1994. The relative value of labeled and unlabeled samples in Pattern Recognition. Ph. D. Dissertation Stanford University. Gabrys, B., & Petrakieva, L. (2004). Combining labelled and unlabelled data in the design of pattern classification systems. International journal of approximate reasoning, 35(3), 251-273. Triguero, I., García, S., & Herrera, F. (2015). Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowledge and Information systems, 42, 245-284. Tasmin, M.; Ishtiak, T.; Ruman, S.U.; Suhan, A.U.R.C.; Islam, N.S.; Jahan, S.; Rahman, R.M. Comparative Study of Classifiers on Human Activity Recognition by Different Feature Engineering Techniques. In Proceedings of the 2020 IEEE 10th Interna-tional Conference on Intelligent Systems (IS), Varna, Bulgaria, 28–30 August 2020; pp. 93–101 Bozkurt, F. A Comparative Study on Classifying Human Activities Using Classical Machine and Deep Learning Methods. Arab. J. Sci. Eng. 2021, 47, 1507–1521. Subasi, A.; Radhwan, M.; Kurdi, R.; Khateeb, K. IoT based mobile healthcare system for human activity recognition. In Pro-ceedings of the 2018 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 25–26 February 2018; pp. 29–34. Maswadi, K.; Ghani, N.A.; Hamid, S.; Rasheed, M.B. Human activity classification using Decision Tree and Naïve Bayes classifiers. Multimed. Tools Appl. 2021, 80, 21709–21726. Wang, A.; Zhao, S.; Zheng, C.; Chen, H.; Liu, L.; Chen, G. HierHAR: Sensor-Based Data-Driven Hierarchical Human Activity Recognition. IEEE Sens. J. 2020, 21, 3353–3365. Demrozi, F.; Turetta, C.; Pravadelli, G. B-HAR: An open-source baseline framework for in depth study of human activity recognition datasets and workflows. arXiv 2021, arXiv:2101.10870. Xu, Z.; Wang, G.; Guo, X. Sensor-based activity recognition of solitary elderly via stigmergy and two layer framework. Eng. Appl. Artif. Intell. 2020, 95, 10385. Hussain, F.; Hussain, F.; Ehatisham-ul-Haq, M.; Azam, M.A. Activity-aware fall detection and recognition based on weara-ble sensors. IEEE Sens. J. 2019, 19, 4528–4536 F. Demrozi, G. Pravadelli, A. Bihorac and P. Rashidi, "Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey," in IEEE Access, vol. 8, pp. 210816-210836, 2020, doi: 10.1109/ACCESS.2020.3037715. Liciotti, D.; Bernardini, M.; Romeo, L.; Frontoni, E. A sequential deep learning application for recognising human activities in smart homes. Neurocomputing 2019, 396, 501–513 Igwe, O.M.; Wang, Y.; Giakos, G.C.; Fu, J. Human activity recognition in smart environments employing margin setting algo-rithm. J. Ambient Intell. Humaniz. Comput. 2020, 1–13. https://doi.org/10.1007/s12652-020-02229-y Oukrich, N. Daily Human Activity Recognition in Smart Home Based on Feature Selection, Neural Network and Load Sig-nature of Appliances. Ph.D. Thesis, Université Mohamed V, Ecole Mohammadia d'Ingénieurs-Université Mohammed V de Rabat-Maroc, Rabat, Morocco, 2019. Damodaran, N.; Haruni, E.; Kokhkharova, M.; Schäfer, J. Device free human activity and fall recognition using WiFi channel state information (CSI). CCF Trans. Pervasive Comput. Interact. 2020, 2, 1–17. Franco, P.; Martínez, J.M.; Kim, Y.C.; Ahmed, M.A. IoT based approach for load monitoring and activity recognition in smart homes. IEEE Access 2021, 9, 45325–45339. Bota, P.; Silva, J.; Folgado, D.; Gamboa, H. A Semi-Automatic Annotation Approach for Human Activity Recognition. Sensors 2019, 19, 501. Mohmed, G.; Lotfi, A.; Langensiepen, C.; Pourabdollah, A. Clustering-based fuzzy finite state machine for human activity recognition. In UK Workshop on Computational Intelligence; Springer: Cham, Switzerland, 2018; pp. 264–275. Brena, R.F.; Garcia-Ceja, E. A crowdsourcing approach for personalization in human activities recognition. Intell. Data Anal. 2017, 21, 721–738. Wang, X.; Lu, Y.; Wang, D.; Liu, L.; Zhou, H. Using Jaccard distance measure for unsupervised activity recognition with smartphone accelerometers. In Proceedings of the Asia-Pacific Web (apweb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, Beijing, China, 7–9 July 2017; Springer: Cham, Switzerland, 2017; pp. 74–83. Zhang, M.; Sawchuk, A.A. USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. In Proceedings of the ACM International Conference on Ubiquitous Computing (UbiComp) Workshop on Situation, Activity and Goal Awareness (SAGAware), Pittsburgh, PA, USA, 5–8 September 2012. 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 Center of Advanced Studies In Adaptative System. CASAS DATASETS. https://casas.wsu.edu/datasets/ Jain, A.; Duin, R.; Mao, J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell.2000, 22, 4–37. Min-Ling Zhang; Zhi-Hun Zhou, Solving multi-instance problems with classifier ensemble based on constructive clustering. DOI 10.1007/s10115-006-0029-3 Nguyen Anthony; Moore Darren; McCowan Iain, Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry. 1-4244-0788-5/07/$20.00 ©2007 IEEE
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dc.publisher.department.none.fl_str_mv	Ciencias de la Computación y Electrónica
dc.publisher.place.none.fl_str_mv	Barranquilla, Colombia
dc.publisher.program.none.fl_str_mv	Maestría en Ingeniería
publisher.none.fl_str_mv	Corporacion Universidad de la Costa
institution	Corporación Universidad de la Costa
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spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Morales Otega RobertoAriza Colpas PaolaPacheco Cuentas, Rosberg YaserMelendez Pertuz FaridPatiño Saucedo JannsDiaz Martinez Jorge2024-10-23T16:32:07Z2024-10-23T16:32:07Z2024https://hdl.handle.net/11323/13501Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Human Activity Recognition (HAR) is a topic of great relevance due to its wide range of applications, with various approaches being proposed to recognize these activities, from comparing signals with thresholds to applying machine learning and deep learning techniques. The development of computational systems capable of performing this recognition and extracting truthful, useful, compact, and natural language-like information is a very active area of knowledge and encompasses a research field that subscribes to an investigative framework, which is the study of daily life activities (ADL), where efforts from researchers in different areas of knowledge come together. This frames the present research and its fundamental objective is to advance in the development of a model that allows solving the problem of human activity recognition through the automatic analysis of datasets based on unsupervised learning techniques. The research required the execution of a series of phases: characterization, experimentation, and evaluation. During the characterization phase, a public human activity recognition dataset, CASAS Kyoto, was selected, which is stored in databases that pre-trained models use to generate useful knowledge, from analyzing patterns, generating predictions, and identifying behavior trends. During the experimentation phase, an ensemble-based model was applied, which utilized the advantages of both supervised and unsupervised methods to consolidate results capable of supporting a closer identification of these activitiesEl reconocimiento de actividades humanas (HAR), es un tema de mucha relevancia debido a su amplia gama de aplicaciones, siendo propuestos diferentes enfoques para reconocer estas actividades, desde la comparación de señales con umbrales hasta la aplicación de técnicas de aprendizaje automático y profundo. El desarrollo de sistemas computacionales capaces de realizar este reconocimiento y extraer una información veraz, útil, compacta y cercana al lenguaje natural, es un área de conocimiento muy activa y comprende un ámbito de investigación que se suscribe a un marco investigativo, que es el estudio de las actividades de la vida diaria (ADL), en la que se aúnan esfuerzos de investigadores de diferentes áreas de conocimiento. Lo que nos enmarca en la presente investigación y su objetivo fundamental es el avanzar en el planteamiento de un modelo que permita resolver el problema de reconocimiento de actividades humanas mediante el análisis automático de conjuntos de datos (Datasets) basados en técnicas aprendizaje no supervisado. La investigación requirió de la ejecución de una serie de fases: caracterización, experimentación y evaluación. Durante la fase de caracterización se seleccionó un conjunto de datos público de reconocimiento de actividades humanas CASAS Kyoto, el cual, se encuentra almacenado en bases de datos que los modelos pre-entrenados utilizan para generar conocimiento útil, desde el análisis de patrones, la generación de predicciones y la identificación de tendencias de comportamiento. Durante la fase de experimentación se aplicó un modelo basado en ensamble donde se utilizó las bondades del método supervidado y no supervisados para consolidar resultados capaces de apoyar a una identificación más cercana de estas actividades.Lista de tablas y figuras 10 -- Introducción 12 -- Contexto 12 -- Mapa del documento 15 -- Problemática abordada y motivación 16 – Justificación 18 – Objetivos 19 --Fundamentación conceptual 20 -- Reconocimiento de actividades humanas diarias (HAR - ADL) 20 -- Aprendizaje supervisado y no supervisado 24 -- Aprendizaje supervisado 25 -- Aprendizaje no supervisado 27 -- Dataset casas kyoto 30 -- Clustering 32 -- Trabajos relacionados 32 -- Aplicaciones de técnicas supervisadas en el reconocimiento de Actividades Humanas 34 -- Aplicaciones de técnicas no supervisadas en el reconocimiento de Actividades Humanas 50 -- Metodología 56 --Fase 1 - Conjunto de Datos Semi-Supervisado 58 -- Fase 2 – Enfoque de agrupación 60 -- Fuzzy clustering 61 -- Agglomerative clustering 61 -- K-means clustering 61 -- Fase 3 – Enfoque de técnicas de clasificación 62 –Bagging 62 --J48 62 --Experimentación 62 -- Fase 1: Experimentación no supervisada 62 -- Escenario No.1: 65 -- Escenario No.2 66 -- Fase 2 y 3: Enfoque de clustering y clasificación 69 --Agglomerative clustering con bagging 69 -- Agglomerative Clustering con J48 70 -- K-means con Bagging 71 -- K-means con J48 72 – Conclusiones 76 –Referencias 78 --Magíster en IngenieríaMaestría84 páginasapplication/pdfspaCorporacion Universidad de la CostaCiencias de la Computación y ElectrónicaBarranquilla, ColombiaMaestría en IngenieríaModelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clusteringTrabajo de grado - MaestríaTextinfo:eu-repo/semantics/masterThesishttp://purl.org/redcol/resource_type/TMinfo:eu-repo/semantics/acceptedVersionOrganización Panamericana de la Salud (OPS). (2023, octubre). Demencia. Available online: https://www.paho.org/es/temas/demenciaSikder, N., & Nahid, A.-A. (2021). KU-HAR: An open dataset for heterogeneous human activity recognition. Pattern Recognition Letters, 146, 46–54.Popescu, A.-C., Mocanu, I., & Cramariuc, B. (2019). PRECIS HAR. Retrieved from https://ieee dataport.org/open-access/precishar (accessed on 30 OctoberMartínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort Asturiano, C. (2019). UP-Fall Detection Dataset: A multimodal approach. Sensors, 19(1988).Van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea, 21–24 September 2008; pp. 1–9.Singla, G.; Cook, D.; Schmitter-Edgecombe, M. Recognizing independent and joint activities among multiple residents in smart environments. Ambient. Intell. Humaniz. Comput. J. 2010, 1, 57–63.Weiss, G.M.; Yoneda, K.; Hayajneh, T. Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living. IEEE Access 2019, 7, 133190–133202.Gallissot, M.; Caelen, J.; Bonnefond, N.; Meillon, B.; Pons, S. Using the Multicom Domus Dataset; Research Report RR-LIG-020; LIG: Grenoble, France, 2011Roggen, D.; Calatroni, A.; Rossi, M.; Holleczek, T.; Tröster, G.; Lukowicz, P.; Pirkl, G.; Bannach, D.; Ferscha, A.; Doppler, J.; et al. Collecting complex activity data sets in highly rich networked sensor environments. In Proceedings of the Seventh International Conference on Networked Sensing Systems (INSS’10), Kassel, Germany, 15–18 June 2010.Cook, D. Learning setting-generalized activity mdoels for smart spaces. IEEE Intell. Syst. 2010, 1.Zhang, M.; Sawchuk, A.A. USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. In Proceedings of the ACM International Conference on Ubiquitous Computing (UbiComp) Workshop on Situation, Activity and Goal Awareness (SAGAware), Pittsburgh, PA, USA, 5–8 September 2012.Logan, B.; Healey, B.J.; Philipose, J.M.; Tapia, E.M.; Intille, S. A long-term evaluation of sensing modalities for activity recognition. In Proceedings of the International Conference on Ubiquitous Computing, Taipei, Taiwan, 17–20 December 2007; Springer: Berlin/Heidelberg, Germany, 2007; pp. 483–500Nugent, C.D.; Mulvenna, M.D.; Hong, X.; Devlin, S. Experiences in the development of a Smart Lab. Int. J. Biomed. Eng. Technol. 2009, 2, 319–331.Schmitter-Edgecombe, M.; Cook, D.J. Assessing the Quality of Activities in a Smart Environment. Methods Inf. Med. 2009, 48, 480–485.Reiss, A.; Stricker, D. Introducing a New Benchmarked Dataset for Activity Monitoring. In Proceedings of the 16th IEEE International Symposium on Wearable Computers (ISWC), Newcastle, UK, 18–22 June 2012.Banos, O.; Garcia, R.; Holgado, J.A.; Damas, M.; Pomares, H.; Rojas, I.; Saez, A.; Villalonga, C. mHealthDroid: A novel framework for agile development of mobile health applications. In Proceedings of the 6th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2014), Belfast, UK, 2–5 December 2014.Barshan, B.; Yüksek, M.C. Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput. J. 2014, 57, 1649–1667Espinilla, M.; Martínez, L.; Medina, J.; Nugent, C. The experience of developing theUJAmI Smart lab. IEEE Access. 2018, 6, 34631–34642.Y. Chen and C. Shen, “Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition,” IEEE Access, vol. 5, pp. 3095–3110, 2017, DOI: 10.1109/ACCESS.2017.2676168.C. A. Ronao and S. B. 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DOI 10.1007/s10115-006-0029-3Nguyen Anthony; Moore Darren; McCowan Iain, Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry. 1-4244-0788-5/07/$20.00 ©2007 IEEEHuman activity recognitionHARDaily life activitiesADLClassification methodsSmart homeClusteringEnsemble methodsReconocimiento de actividades humanasActividades de la vida diariaMétodos de clasificaciónHogar inteligenteClusteringMétodos ensambladosPublicationORIGINALModelo de aprendizaje no supervisado aplicado a un conjunto de datos de Casas Kyoto basado en un enfoque de clustering.pdfModelo de aprendizaje no supervisado aplicado a un conjunto de datos de Casas Kyoto basado en un enfoque de clustering.pdfapplication/pdf1479116https://repositorio.cuc.edu.co/bitstreams/18dab69c-6528-425f-99ee-1374f625a087/download4e581baa275f0f0b88fb52e3d402ad1bMD51LICENSElicense.txtlicense.txttext/plain; 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>


Modelo de aprendizaje no supervisado aplicado a un conjunto de datos de casas kyoto basado en un enfoque de clustering

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