Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning

AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and...

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Autores:
García-Restrepo, Johanna
Ariza-Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Urina-Triana, Miguel
De-la-Hoz-Franco, Emiro
Díaz-Martínez, Jorge Luis
Butt, Shariq Aziz
Molina_Estren, Diego
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/8604
Acceso en línea:
https://hdl.handle.net/20.500.12442/8604
https://doi.org/10.1016/j.procs.2021.07.069
https://www.sciencedirect.com/science/article/pii/S1877050921014721?via%3Dihub
Palabra clave:
HAR
Human Activity Recognition
Machine Learning
ADL
Activity Daily Living
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.title.eng.fl_str_mv Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
spellingShingle Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
HAR
Human Activity Recognition
Machine Learning
ADL
Activity Daily Living
title_short Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_full Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_fullStr Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_full_unstemmed Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
title_sort Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning
dc.creator.fl_str_mv García-Restrepo, Johanna
Ariza-Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Urina-Triana, Miguel
De-la-Hoz-Franco, Emiro
Díaz-Martínez, Jorge Luis
Butt, Shariq Aziz
Molina_Estren, Diego
dc.contributor.author.none.fl_str_mv García-Restrepo, Johanna
Ariza-Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Urina-Triana, Miguel
De-la-Hoz-Franco, Emiro
Díaz-Martínez, Jorge Luis
Butt, Shariq Aziz
Molina_Estren, Diego
dc.subject.eng.fl_str_mv HAR
Human Activity Recognition
Machine Learning
ADL
Activity Daily Living
topic HAR
Human Activity Recognition
Machine Learning
ADL
Activity Daily Living
description AI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effective
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-10-01T21:25:05Z
dc.date.available.none.fl_str_mv 2021-10-01T21:25:05Z
dc.date.issued.none.fl_str_mv 2021
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dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.spa.spa.fl_str_mv Artículo científico
dc.identifier.issn.none.fl_str_mv 18770509
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/8604
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.procs.2021.07.069
dc.identifier.url.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S1877050921014721?via%3Dihub
identifier_str_mv 18770509
url https://hdl.handle.net/20.500.12442/8604
https://doi.org/10.1016/j.procs.2021.07.069
https://www.sciencedirect.com/science/article/pii/S1877050921014721?via%3Dihub
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
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eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv pdf
dc.publisher.eng.fl_str_mv Elsevier
dc.source.eng.fl_str_mv Procedia Computer Science
dc.source.none.fl_str_mv Vol. 191 (2021)
institution Universidad Simón Bolívar
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spelling García-Restrepo, Johannaa77aa041-530f-48b4-a85a-f22d060061c6Ariza-Colpas, Paola Patriciaf768dde3-9fd1-49bf-b93a-5ef890613c99Oñate-Bowen, Alvaro Agustína7a4dcf7-8485-44e9-9804-31d6d448aa2bSuarez-Brieva, Eydy del Carmen0a97329d-3147-4b43-8016-115c47603699Urina-Triana, Migueld749d19c-0dae-4d0b-8e9a-6d623d682f9eDe-la-Hoz-Franco, Emiro1fca6b8d-83c3-4b59-865d-dbfac226e46aDíaz-Martínez, Jorge Luiseea0bb37-eb5b-440f-9714-90abd4983e88Butt, Shariq Aziz30654f82-ff57-4c6d-861f-2f6609dc24cfMolina_Estren, Diego8c891839-4704-4658-bd73-814fa67b4e3d2021-10-01T21:25:05Z2021-10-01T21:25:05Z202118770509https://hdl.handle.net/20.500.12442/8604https://doi.org/10.1016/j.procs.2021.07.069https://www.sciencedirect.com/science/article/pii/S1877050921014721?via%3DihubAI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effectivepdfengElsevierAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer ScienceVol. 191 (2021)HARHuman Activity RecognitionMachine LearningADLActivity Daily LivingPredictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learninginfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1agi S.Z., Burk R.D., Potter H.R. Back disorders and rehabilitation achievement Journal of Chronic Diseases, 18 (2) (1965), pp. 181-197 https://doi.org/10.1016/0021-9681(65)90101-3Lladó M.R., Código H., Lennin A., Quiroz P., Lima -Perú V. ENTORNO DOMÓTICO ADAPTADO A PERSONAS CON DISCAPACIDAD FÍSICA UTILIZANDO MODELOS OCULTOS DE MARKOV Tesis para optar el Título Profesional de Ingeniero de Sistemas Repositorio Institucional - Ulima, Universidad de Lima (2020) http://repositorio.ulima.edu.pe/handle/20.500.12724/11664Ronao C.A., Cho S.B. Human activity recognition with smartphone sensors using deep learning neural networks Expert Systems with Applications, 59 (2016), pp. 235-244 https://doi.org/10.1016/j.eswa.2016.04.032Capela N.A., Lemaire E.D., Baddour N. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients PLoS ONE, 10 (4) (2015), p. e0124414 https://doi.org/10.1371/journal.pone.0124414Gudivada, 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/318432363Ren, 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.2003.1238308Galván-Tejada C.E., Galván-Tejada J.I., Celaya-Padilla J.M., DelgadoContreras J.R., Magallanes-Quintanar R., Martinez-Fierro M.L., GarzaVeloz I., López-Hernández Y., Gamboa-Rosales H. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks Mobile Information Systems, 2016 (2016), pp. 1-10 https://doi.org/10.1155/2016/1784101Eddy S.R. Profile hidden Markov models Academic.Oup.Com, 144 (9) (1998), pp. 755-763 https://academic.oup.com/bioinformatics/articleabstract/14/9/755/259550Envejecimiento y salud. (2018, February 5)Shah, C. (2020). Supervised Learning. In 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 Artificial Intelligence Review, 33 (4) (2010), pp. 275-306 https://doi.org/10.1007/s10462-010-9156-zCaruana R., Niculescu-Mizil A. An empirical comparison of supervised learning algorithms ACM International Conference Proceeding Series, 148 (2006), pp. 161-168 https://doi.org/10.1145/1143844.1143865Mejia-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.2017.8356374Crandall, A. S. (2011). BEHAVIOMETRICS FOR MULTIPLE RESIDENTS IN A SMART ENVIRONMENT. https://SCI-HUB.si/http://research.wsulibs.wsu.edu/xmlui/handle/2376/2855Hoey J., Pltz T., Jackson D., Monk A., Pham C., Olivier P. Rapid specification and automated generation of prompting systems to assist people with dementia Pervasive and Mobile Computing, 7 (3) (2011), pp. 299-318 https://doi.org/10.1016/j.pmcj.2010.11.007Fahad, 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.2015.724837310Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P. SMOTE: Synthetic Minority Over-sampling Technique Journal of Artificial Intelligence Research, 16 (2002) https://SCI-HUB.si/http://www.jair.org/index.php/jair/article/view/10302Ruan Y.X., Lin H.T., Tsai M.F. Improving ranking performance with cost-sensitive ordinal classification via regression Information Retrieval, 17 (1) (2014), pp. 1-20 https://doi.org/10.1007/s10791-013-9219-2Ho T.K. The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (8) (1998), pp. 832-844 https://doi.org/10.1109/34.709601Breiman L. Bagging predictors Machine Learning, 24 (2) (1996), pp. 123-140 https://doi.org/10.1007/bf00058655Nagalla R., Pothuganti P., Pawar D.S. Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests Procedia Computer Science, 109 (2017), pp. 474-481 https://doi.org/10.1016/j.procs.2017.05.312Salzberg S.L. C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 Machine Learning, 16 (3) (1994), pp. 235-240 https://doi.org/10.1007/bf00993309Kumar K., Kumar G., Kumar Y. Feature Selection Approach for Intrusion Detection System International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2 (5) (2013), pp. 47-53Ariza Colpas P., Vicario E., De-La-Hoz-Franco E., Pineres-Melo M., Oviedo-Carrascal A., Patara F. Unsupervised human activity recognition using the clustering approach: A review Sensors, 20 (9) (2020), p. 2702Chandra S., Maheshkar S. Verification of static signature pattern based on random subspace, REP tree and bagging Multimedia Tools and Applications, 76 (18) (2017), pp. 19139-19171 https://doi.org/10.1007/s11042-017-4531-2Asaju L.B., Shola P.B., Franklin N., Abiola H.M. Intrusion Detection System on a Computer Network Using an Ensemble of Randomizable Filtered Classifier, K-Nearest … Ftstjournal.Com, 2 (1) (2017), pp. 550-553 http://www.ftstjournal.comKalmegh S. 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) (2015), pp. 438-446 http://www.ijiset.comRajput A., Aharwal R.P., Dubey M., Saxena S.P., Raghuvanshi M. J48 and JRIP rules for e-governance data International Journal of Computer Science and Security, 5 (2) (2011), pp. 201-207 11Cai Y.D., Feng K.Y., Lu W.C., Chou K.C. Using LogitBoost classifier to predict protein structural classes Journal of Theoretical Biology, 238 (1) (2006), pp. 172-176 https://doi.org/10.1016/j.jtbi.2005.05.034Qian H., Mao Y., Xiang W., Wang Z. Recognition of human activities using SVM multi-class classifier Pattern Recognition Letters, 31 (2) (2010), pp. 100-111 https://doi.org/10.1016/j.patrec.2009.09.019Suykens J.A.K., Vandewalle J. Training multilayer perceptron classifiers based on a modified support vector method IEEE Transactions on Neural Networks, 10 (4) (1999), pp. 907-911 https://doi.org/10.1109/72.774254Khalajzadeh H., Mansouri M., Teshnehlab M. Face recognition using convolutional neural network and simple logistic classifier Advances in Intelligent Systems and Computing, 223 (2014), pp. 197-207 https://doi.org/10.1007/978-3-319-00930-8_18Choudhury, 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.2015.7225395Carlos A., D’negri E., De Vito E.L., Zadeh L.A. Introducción al razonamiento aproximado: lógica difusa Revista Argentina de Medicina Respiratoria Año, 6 (2006)DANE. Archivo Nacional de Datos ANDA. 2014. [Citado Marzo 20,2016]. Available in: http://formularios.dane.gov.co/Anda_4_1/index.php/homeLópez Saca F., Ferreyra Ramírez A., Avilés Cruz C., Villegas Cortez J., Zúñiga López A., Rodrigez Martinez E. Preprocesamiento de bases de datos de imágenes para mejorar el rendimiento de redes neuronales convolucionales Research in Computing Science, 147 (7) (2018), pp. 35-45 https://doi.org/10.13053/rcs-147-7-3Marcondes C.H., Almeida Campos M. 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