Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition
One of the technical aspects that contribute to improving the quality of life for older adults is the automation of physical spaces using sensors and actuators, which facilitates the performance of their daily activities. The interaction between individuals and their environment enables the detectio...
- Autores:
-
García Restrepo, Johanna Karinna
Ariza Colpas, Paola Patricia
Butt Aziz, Shariq
Piñeres Melo, Marlon Alberto
Naz, Sumera
De la hoz Franco, Emiro
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13664
- Acceso en línea:
- https://hdl.handle.net/11323/13664
https://repositorio.cuc.edu.co/
- Palabra clave:
- Activity of Daily Living
Classification Methods
Human Activity Recognition
Selection Methods
Smart home
- Rights
- closedAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| title |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| spellingShingle |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition Activity of Daily Living Classification Methods Human Activity Recognition Selection Methods Smart home |
| title_short |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| title_full |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| title_fullStr |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| title_full_unstemmed |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| title_sort |
Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognition |
| dc.creator.fl_str_mv |
García Restrepo, Johanna Karinna Ariza Colpas, Paola Patricia Butt Aziz, Shariq Piñeres Melo, Marlon Alberto Naz, Sumera De la hoz Franco, Emiro |
| dc.contributor.author.none.fl_str_mv |
García Restrepo, Johanna Karinna Ariza Colpas, Paola Patricia Butt Aziz, Shariq Piñeres Melo, Marlon Alberto Naz, Sumera De la hoz Franco, Emiro |
| dc.subject.proposal.eng.fl_str_mv |
Activity of Daily Living Classification Methods Human Activity Recognition Selection Methods Smart home |
| topic |
Activity of Daily Living Classification Methods Human Activity Recognition Selection Methods Smart home |
| description |
One of the technical aspects that contribute to improving the quality of life for older adults is the automation of physical spaces using sensors and actuators, which facilitates the performance of their daily activities. The interaction between individuals and their environment enables the detection of abnormal patterns that may arise from a decline in their cognitive abilities. In this study, we evaluate the CASAS Kyoto dataset from WSU University, which provides information on the daily living activities of individuals within an indoor environment. We developed a model to predict activities such as Cleaning, Cooking, Eating, Washing hands, and Phone Call. A novel approach is proposed, which involves preprocessing and segmenting the dataset using sliding windows. Furthermore, we conducted experiments with various classifiers to determine the optimal choice for the model. The final model utilizes the regression classification technique and is trained on a reduced dataset containing only 5 features. It achieves outstanding results, with a Recall of 99.80% and a ROC area of 100%. |
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2023 |
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2024-11-12T12:53:46Z |
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García-Restrepo, JK., Ariza-Colpas, P.P., Butt-Aziz, S., Piñeres-Melo, M.A., Naz, S., De-la-hoz-Franco, E. (2023). Evaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_19 |
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0302-9743 |
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https://hdl.handle.net/11323/13664 |
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10.1007/978-3-031-42823-4_19 |
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1611-3349 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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García-Restrepo, JK., Ariza-Colpas, P.P., Butt-Aziz, S., Piñeres-Melo, M.A., Naz, S., De-la-hoz-Franco, E. (2023). Evaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_19 0302-9743 10.1007/978-3-031-42823-4_19 1611-3349 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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Welcome to CASAS. http://casas.wsu.edu/datasets/. Accessed 11 Sep 2022 Fettes, L., Bone, A.E., Etkind, S.N., Ashford, S., Higginson, I.J., Maddocks, M. Disability in basic activities of daily living is associated with symptom burden in older people with advanced cancer or chronic obstructive pulmonary disease: A secondary data analysis (2021) J. Pain Symptom Manage., 61 (6), pp. 1205-1214. Carlozzi, N.E. Daily variation in sleep quality is associated with health-related quality of life in people with spinal cord injury (2021) Arch. Phys. Med. Rehabil., Vich, G., Delclòs-Alió, X., Maciejewska, M., Marquet, O., Schipperijn, J., Miralles-Guasch, C. Contribution of park visits to daily physical activity levels among older adults: Evidence using GPS and accelerometer data (2021) Urban Forestry Urban Green, 63. Ariza-Colpas, P.P. Human activity recognition data analysis: History, evolutions, and new trends (2022) Sensors, 22 (9), p. 3401. Itoh, S. Acceptance of care technologies to support activities of daily living by middle-aged and older adults in Japan: A cross-sectional study (2021) Int. J. Nurs. Stud. Adv., 3. Ding, D., Cooper, R.A., Pasquina, P.F., Fici-Pasquina, L. Sensor technology for smart homes (2011) Maturitas, 69 (2), pp. 131-136. Khalifa, S., Lan, G., Hassan, M., Seneviratne, A., Das, S.K. HARKE: Human activity recognition from kinetic energy harvesting data in wearable devices (2018) IEEE Trans. Mob. Comput., 17 (6), pp. 1353-1368. Cardoso, H.L., Moreira, J.M. Human activity recognition by means of online semi-supervised learning (2016) 17Th IEEE International Conference on Mobile Data Management (MDM), pp. 75- 77. 2016 Calabria-Sarmiento, J.C. (2018) Software Applications to Health Sector: A Systematic Review of Literature, Islam, A. Android application based smart home automation system using Internet of Things (2018) 2018 3Rd International Conference for Convergence in Technology (I2CT), pp. 1- 9. Jalal, A., Kamal, S., Kim, D. A depth video-based human detection and activity recognition using multi-features and embedded hidden markov models for health care monitoring systems (2017) Int. J. Interact. Multimedia Artific. Intell., 4 (4), p. 54. He, Y., Li, Y., Yin, C. Falling-incident detection and alarm by smartphone with multimedia messaging service (MMS) (2012) E-Health Telecommun. Syst. Networks, 1 (1), pp. 1-5. Tabuenca Dopico, P., Sánchez Espeso, P.P., Villar Bonet, E. Realisation of an intelligent planner for high-level synthesis (1993) 8Th Integrated Circuit Design Conference, pp. 315-319. Accessed 11 Sep 2022 Chen, Y., Shen, C. Performance analysis of smartphone-sensor behavior for human activity recognition (2017) IEEE Access, 5, pp. 3095-3110. Ronao, C.A., Cho, S.B. Human activity recognition with smartphone sensors using deep learning neural networks (2016) Expert Syst. Appl., 59, pp. 235-244. Capela, N.A., Lemaire, E.D., Baddour, N. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients (2015) Plos ONE, 10 (4). Gudivada, V.N., Ding, J., Apon, A.: 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,” no. October, pp. 1–20 (2017) https://www.researchgate.net/pub lication/318432363. Accessed 11 Sep 2022 Ren, X., Malik, J. Learning a classification model for segmentation (2003) Proceedings of the IEEE International Conference on Computer Vision, 1, pp. 10- 17. Galván-Tejada, C.E. An analysis of audio features to develop a human activity recognition model using genetic algorithms, random forests, and neural networks (2016) Mob. Inf. Syst., 2016, p. 1. Eddy, S.R.: Profile hidden Markov models, academic.oup.com, vol. 144, no. 9, pp. 755– 63 (1998). https://academic.oup.com/bioinformatics/article-abstract/14/9/755/259550. Accessed 11 Sep. 2022 Shah, C. Supervised Learning (2020) A Hands-On Introduction to Data Science, pp. 235-289. Nettleton, D.F., Orriols-Puig, A., Fornells, A. A study of the effect of different types of noise on the precision of supervised learning techniques (2010) Artif. Intell. Rev., 33 (4), pp. 275-306. Caruana, R., Niculescu-Mizil, A. An empirical comparison of supervised learning algorithms (2006) ACM International Conference Proceeding Series, 148, pp. 161-168. Mejia-Ricart, L.F., Helling, P., Olmsted, A. Evaluate action primitives for human activity recognition using unsupervised learning approach (2018) 2017 12Th International Conference for Internet Technology and Secured Transactions, ICITST 2017, Pp. 186–188, Crandall, A.S., Cook, D.J. (2013) Behaviometrics for Identifying Smart Home Residents, pp. 55-71. Chen, L., Nugent, C.D., Wang, H. A knowledge-driven approach to activity recognition in smart homes (2012) IEEE Trans. Knowl. Data Eng., 24 (6), pp. 961-974. Hoey, J., Pltz, T., Jackson, D., Monk, A., Pham, C., Olivier, P. Rapid specification and automated generation of prompting systems to assist people with dementia (2011) Pervasive Mob. Comput., 7 (3), pp. 299-318. Fahad, L.G., Tahir, S.F., Rajarajan, M. Feature selection and data balancing for activity recognition in smart homes (2015) 2015 IEEE International Conference on Communications (ICC), pp. 512-517. Du, Y., Lim, Y., Tan, Y. A novel human activity recognition and prediction in smart home based on interaction (2019) Sensors, 19 (20). Johanna, G.R. Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning (2021) Procedia Comput. Sci., 191, pp. 361-366. Ma, C., Li, W., Cao, J., Du, J., Li, Q., Gravina, R. Adaptive sliding window based activity recognition for assisted livings (2020) Inform. Fus., 53, pp. 55-65. |
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Atribución 4.0 Internacional (CC BY 4.0)© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbGarcía Restrepo, Johanna KarinnaAriza Colpas, Paola PatriciaButt Aziz, ShariqPiñeres Melo, Marlon AlbertoNaz, SumeraDe la hoz Franco, Emiro2024-11-12T12:53:46Z2024-11-12T12:53:46Z2023-09-15García-Restrepo, JK., Ariza-Colpas, P.P., Butt-Aziz, S., Piñeres-Melo, M.A., Naz, S., De-la-hoz-Franco, E. (2023). Evaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_190302-9743https://hdl.handle.net/11323/1366410.1007/978-3-031-42823-4_191611-3349Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/One of the technical aspects that contribute to improving the quality of life for older adults is the automation of physical spaces using sensors and actuators, which facilitates the performance of their daily activities. The interaction between individuals and their environment enables the detection of abnormal patterns that may arise from a decline in their cognitive abilities. In this study, we evaluate the CASAS Kyoto dataset from WSU University, which provides information on the daily living activities of individuals within an indoor environment. We developed a model to predict activities such as Cleaning, Cooking, Eating, Washing hands, and Phone Call. A novel approach is proposed, which involves preprocessing and segmenting the dataset using sliding windows. Furthermore, we conducted experiments with various classifiers to determine the optimal choice for the model. The final model utilizes the regression classification technique and is trained on a reduced dataset containing only 5 features. It achieves outstanding results, with a Recall of 99.80% and a ROC area of 100%.17 páginasapplication/pdfengSpringer VerlagGermanyhttps://link.springer.com/chapter/10.1007/978-3-031-42823-4_19Evaluating techniques based on supervised learning methods in casas kyoto dataset for human activity recognitionArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceLecture Notes in Computer ScienceWelcome to CASAS. http://casas.wsu.edu/datasets/. Accessed 11 Sep 2022Fettes, L., Bone, A.E., Etkind, S.N., Ashford, S., Higginson, I.J., Maddocks, M. Disability in basic activities of daily living is associated with symptom burden in older people with advanced cancer or chronic obstructive pulmonary disease: A secondary data analysis (2021) J. Pain Symptom Manage., 61 (6), pp. 1205-1214.Carlozzi, N.E. Daily variation in sleep quality is associated with health-related quality of life in people with spinal cord injury (2021) Arch. Phys. Med. Rehabil.,Vich, G., Delclòs-Alió, X., Maciejewska, M., Marquet, O., Schipperijn, J., Miralles-Guasch, C. Contribution of park visits to daily physical activity levels among older adults: Evidence using GPS and accelerometer data (2021) Urban Forestry Urban Green, 63.Ariza-Colpas, P.P. Human activity recognition data analysis: History, evolutions, and new trends (2022) Sensors, 22 (9), p. 3401.Itoh, S. Acceptance of care technologies to support activities of daily living by middle-aged and older adults in Japan: A cross-sectional study (2021) Int. J. Nurs. Stud. Adv., 3.Ding, D., Cooper, R.A., Pasquina, P.F., Fici-Pasquina, L. Sensor technology for smart homes (2011) Maturitas, 69 (2), pp. 131-136.Khalifa, S., Lan, G., Hassan, M., Seneviratne, A., Das, S.K. HARKE: Human activity recognition from kinetic energy harvesting data in wearable devices (2018) IEEE Trans. Mob. Comput., 17 (6), pp. 1353-1368.Cardoso, H.L., Moreira, J.M. Human activity recognition by means of online semi-supervised learning (2016) 17Th IEEE International Conference on Mobile Data Management (MDM), pp. 75- 77. 2016Calabria-Sarmiento, J.C. (2018) Software Applications to Health Sector: A Systematic Review of Literature,Islam, A. Android application based smart home automation system using Internet of Things (2018) 2018 3Rd International Conference for Convergence in Technology (I2CT), pp. 1- 9.Jalal, A., Kamal, S., Kim, D. A depth video-based human detection and activity recognition using multi-features and embedded hidden markov models for health care monitoring systems (2017) Int. J. Interact. Multimedia Artific. Intell., 4 (4), p. 54.He, Y., Li, Y., Yin, C. Falling-incident detection and alarm by smartphone with multimedia messaging service (MMS) (2012) E-Health Telecommun. Syst. Networks, 1 (1), pp. 1-5.Tabuenca Dopico, P., Sánchez Espeso, P.P., Villar Bonet, E. Realisation of an intelligent planner for high-level synthesis (1993) 8Th Integrated Circuit Design Conference, pp. 315-319. Accessed 11 Sep 2022Chen, Y., Shen, C. Performance analysis of smartphone-sensor behavior for human activity recognition (2017) IEEE Access, 5, pp. 3095-3110.Ronao, C.A., Cho, S.B. Human activity recognition with smartphone sensors using deep learning neural networks (2016) Expert Syst. Appl., 59, pp. 235-244.Capela, N.A., Lemaire, E.D., Baddour, N. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients (2015) Plos ONE, 10 (4).Gudivada, V.N., Ding, J., Apon, A.: 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,” no. October, pp. 1–20 (2017) https://www.researchgate.net/pub lication/318432363. Accessed 11 Sep 2022Ren, X., Malik, J. Learning a classification model for segmentation (2003) Proceedings of the IEEE International Conference on Computer Vision, 1, pp. 10- 17.Galván-Tejada, C.E. An analysis of audio features to develop a human activity recognition model using genetic algorithms, random forests, and neural networks (2016) Mob. Inf. Syst., 2016, p. 1.Eddy, S.R.: Profile hidden Markov models, academic.oup.com, vol. 144, no. 9, pp. 755– 63 (1998). https://academic.oup.com/bioinformatics/article-abstract/14/9/755/259550. Accessed 11 Sep. 2022Shah, C. Supervised Learning (2020) A Hands-On Introduction to Data Science, pp. 235-289.Nettleton, D.F., Orriols-Puig, A., Fornells, A. A study of the effect of different types of noise on the precision of supervised learning techniques (2010) Artif. Intell. Rev., 33 (4), pp. 275-306.Caruana, R., Niculescu-Mizil, A. An empirical comparison of supervised learning algorithms (2006) ACM International Conference Proceeding Series, 148, pp. 161-168.Mejia-Ricart, L.F., Helling, P., Olmsted, A. Evaluate action primitives for human activity recognition using unsupervised learning approach (2018) 2017 12Th International Conference for Internet Technology and Secured Transactions, ICITST 2017, Pp. 186–188,Crandall, A.S., Cook, D.J. (2013) Behaviometrics for Identifying Smart Home Residents, pp. 55-71.Chen, L., Nugent, C.D., Wang, H. A knowledge-driven approach to activity recognition in smart homes (2012) IEEE Trans. Knowl. Data Eng., 24 (6), pp. 961-974.Hoey, J., Pltz, T., Jackson, D., Monk, A., Pham, C., Olivier, P. Rapid specification and automated generation of prompting systems to assist people with dementia (2011) Pervasive Mob. Comput., 7 (3), pp. 299-318.Fahad, L.G., Tahir, S.F., Rajarajan, M. Feature selection and data balancing for activity recognition in smart homes (2015) 2015 IEEE International Conference on Communications (ICC), pp. 512-517.Du, Y., Lim, Y., Tan, Y. A novel human activity recognition and prediction in smart home based on interaction (2019) Sensors, 19 (20).Johanna, G.R. Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning (2021) Procedia Comput. Sci., 191, pp. 361-366.Ma, C., Li, W., Cao, J., Du, J., Li, Q., Gravina, R. Adaptive sliding window based activity recognition for assisted livings (2020) Inform. Fus., 53, pp. 55-65.26925314164Activity of Daily LivingClassification MethodsHuman Activity RecognitionSelection MethodsSmart homePublicationORIGINALEvaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition.pdfEvaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition.pdfapplication/pdf160796https://repositorio.cuc.edu.co/bitstreams/fe179d5a-66e2-4038-84fa-9db5ba0ac2dd/downloada5bdeae896f8573072bfe03a64baeef2MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/cfe923cc-d363-4d9d-8dbe-032695c5ea48/download73a5432e0b76442b22b026844140d683MD52TEXTEvaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition.pdf.txtEvaluating Techniques Based on Supervised Learning Methods in Casas Kyoto Dataset for Human Activity Recognition.pdf.txtExtracted 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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>
 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