Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset

Reminders are a core component of many assistive technology systems and are aimed specifically at helping people with dementia function more independently by compensating for cognitive deficits. These technologies are often utilized for prospective reminding, reminiscence, or within coaching-based s...

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Autores:: Ariza-Colpas, Paola
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Oviedo-Carrascal, Ana
Urina Triana, Miguel
Piñeres-Melo, Marlon
Butt, Shariq Aziz,
Collazos Morales, Carlos Andrés
Ramayo González, Ramón Enrique

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Simón Bolívar

Repositorio:: Repositorio Digital USB

Idioma:: eng

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dc.title.eng.fl_str_mv	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
title	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
spellingShingle	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset Machine learning HAR ADL Human Activity Recognition Activity Daily Living VanKasteren Dataset
title_short	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
title_full	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
title_fullStr	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
title_full_unstemmed	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
title_sort	Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset
dc.creator.fl_str_mv	Ariza-Colpas, Paola Oñate-Bowen, Alvaro Agustín Suarez-Brieva, Eydy del Carmen Oviedo-Carrascal, Ana Urina Triana, Miguel Piñeres-Melo, Marlon Butt, Shariq Aziz, Collazos Morales, Carlos Andrés Ramayo González, Ramón Enrique
dc.contributor.author.none.fl_str_mv	Ariza-Colpas, Paola Oñate-Bowen, Alvaro Agustín Suarez-Brieva, Eydy del Carmen Oviedo-Carrascal, Ana Urina Triana, Miguel Piñeres-Melo, Marlon Butt, Shariq Aziz, Collazos Morales, Carlos Andrés Ramayo González, Ramón Enrique
dc.subject.eng.fl_str_mv	Machine learning HAR ADL Human Activity Recognition Activity Daily Living VanKasteren Dataset
topic	Machine learning HAR ADL Human Activity Recognition Activity Daily Living VanKasteren Dataset
description	Reminders are a core component of many assistive technology systems and are aimed specifically at helping people with dementia function more independently by compensating for cognitive deficits. These technologies are often utilized for prospective reminding, reminiscence, or within coaching-based systems. Traditionally, reminders have taken the form of nontechnology based aids, such as diaries, notebooks, cue cards and white boards. This article is based on the use of machine learning algorithms for the detection of Alzheimer’s disease. In the experimentation, the LWL, SimpleLogistic, Logistic, MultiLayerPercepton and HiperPipes algorithms were used. The result showed that the LWL algorithm produced the following results: Accuracy 98.81%, Precission 100%, Recall 97.62% and F- measure 98.80%
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-01T21:49:43Z
dc.date.available.none.fl_str_mv	2021-10-01T21:49:43Z
dc.date.issued.none.fl_str_mv	2021
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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/20.500.12442/8605
dc.identifier.doi.none.fl_str_mv	https://doi.org/10.1016/j.procs.2021.07.070
dc.identifier.url.none.fl_str_mv	https://www.sciencedirect.com/science/article/pii/S1877050921014733?via%3Dihub
identifier_str_mv	18770509
url	https://hdl.handle.net/20.500.12442/8605 https://doi.org/10.1016/j.procs.2021.07.070 https://www.sciencedirect.com/science/article/pii/S1877050921014733?via%3Dihub
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.rights.*.fl_str_mv	Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv	openAccess
dc.format.mimetype.eng.fl_str_mv	pdf
dc.publisher.spa.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	Ariza-Colpas, Paolab278dc48-7bf5-4214-9a4d-82d62cd91884Oñate-Bowen, Alvaro Agustína7a4dcf7-8485-44e9-9804-31d6d448aa2bSuarez-Brieva, Eydy del Carmen0a97329d-3147-4b43-8016-115c47603699Oviedo-Carrascal, Anaa75be05e-e24d-4414-a9a7-e60cfe7faa19Urina Triana, Miguel1afc0065-d873-4d52-883f-11592a768bbfPiñeres-Melo, Marlonfffea554-a38b-423d-afba-716d93881eeeButt, Shariq Aziz,53d2cd3e-db34-43ca-a3d9-3d60a1db24f3Collazos Morales, Carlos Andrésf791ea21-2cd5-4cf7-b24d-b7372fab23bcRamayo González, Ramón Enriqueb0f77877-7958-474b-9f0c-ebcc38befb892021-10-01T21:49:43Z2021-10-01T21:49:43Z202118770509https://hdl.handle.net/20.500.12442/8605https://doi.org/10.1016/j.procs.2021.07.070https://www.sciencedirect.com/science/article/pii/S1877050921014733?via%3DihubReminders are a core component of many assistive technology systems and are aimed specifically at helping people with dementia function more independently by compensating for cognitive deficits. These technologies are often utilized for prospective reminding, reminiscence, or within coaching-based systems. Traditionally, reminders have taken the form of nontechnology based aids, such as diaries, notebooks, cue cards and white boards. This article is based on the use of machine learning algorithms for the detection of Alzheimer’s disease. In the experimentation, the LWL, SimpleLogistic, Logistic, MultiLayerPercepton and HiperPipes algorithms were used. The result showed that the LWL algorithm produced the following results: Accuracy 98.81%, Precission 100%, Recall 97.62% and F- measure 98.80%pdfengElsevierAttribution-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)Machine learningHARADLHuman Activity RecognitionActivity Daily LivingVanKasteren DatasetMachine Learning approach applied to Human Activity Recognition – An application to the VanKasteren datasetinfo:eu-repo/semantics/articleArtículo científicohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Sohn, T., Li, K. A., Lee, G., Smith, I., Scott, J., & Griswold, W. G. (2005, September). Place-its: A study of location-based reminders on mobile phones. In International Conference on Ubiquitous Computing (pp. 232–250). Springer, Berlin, Heidelberg.Sumi Helal, Carlos Giraldo, Youssef Kaddoura, Choonhwa Lee, Hicham El Zabadani, and William Mann. Smart phone based cognitive assistant. In UbiHealth 2003: The 2nd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications, 2003.Pollock P.M., Harper U.L., Hansen K.S., Yudt L.M., Stark M., Robbins C.M., Salem G. High frequency of BRAF mutations in nevi Nature genetics, 33 (1) (2003), pp. 19-20Oliver Zangwill Centre Neuropsychological Rehabilitation Accessed December 2014. NeuroPage. Online, http://www.neuropage.nhs.uk/, 2014.Barbara A Wilson, Jonathan J Evans, Hazel Emslie, Vlastimil Malinek Evaluation of neuropage: a new memory aid Journal of Neurology, Neurosurgery & Psychiatry, 63 (1) (1997), pp. 113-115Barbara A Wilson, Helena Scott, Jonathan Evans, Hazel Emslie Preliminary report of a neuropage service within a health care system NeuroRehabilitation, 18 (1) (2003), pp. 3-8Wilson HC Emslie, K Quirk, JJ Evans Reducing everyday memory and planning problems by means of a paging system: a randomised control crossover study Journal of Neurology, Neurosurgery & Psychiatry, 70 (4) (2001), pp. 477-482Zhou J., Gennatas E.D., Kramer J.H., Miller B.L., Seeley W.W. Predicting regional neurodegeneration from the healthy brain functional connectome Neuron, 73 (6) (2012), pp. 1216-1227SASMITA ati nneli annisto, arita annele oivunen, and aritta nneli lim ki. se of mobile phone text message reminders in health care services: A narrative literature review Journal of medical Internet research, 16 (10) (2014)Marcia Vervloet, Liset van Dijk, Jacqueline Santen-Reestman, Bas van Vlijmen, Marcel L Bouvy, Dinny H de Bakker Improving medication adherence in diabetes type 2 patients through real time medication monitoring: a randomised controlled trial to evaluate the effect of monitoring patients’ medication use combined with short message service (sms) reminders B C health services research, 11 (1) (2011), p. 5Rebecca Guy, Jane Hocking, Handan Wand, Sam Stott, Hammad Ali, John Kaldor How effective are short message service reminders at increasing clinic attendance? a meta-analysis and systematic review Health services research, 47 (2) (2012), pp. 614-632McLean, S., Gee, M., Booth, A., Salway, S., Nancarrow, S., Cobb, M., & Bhanbhro, S. (2014). Targeting the Use of Reminders and Notifications for Uptake by Populations (TURNUP): a systematic review and evidence synthesis.De Jongh T., Gurol‐Urganci I., Vodopivec‐Jamsek V., Car J., Atun R. Mobile phone messaging for facilitating self‐ management of long‐term illnesses Cochrane Database of Systematic Reviews (2012) (12)Sonja O’Neill, Sarah ason, Guido Parente, Ark P Donnelly, Christopher D Nugent, Sally McClean, Bryan Scotney, David Craig Video reminders as cognitive prosthetics for people with dementia Ageing International, 36 (2) (2011), pp. 267-282Zhang, S.I. Clean, C.D. Nugent, S. O’neill, P. Donnelly, L. Galway, B.W. Scotney, I. Cleland “Predictive Model for Assistive Technology Adoption for People with Dementia” Journal of Biomedical and Health Informatics (99) (2013), p. 1,1Hartin P.J., Nugent C.D., McClean S.I., Cleland I., Norton M.C., Sanders C., Tschanz J.T. Identification of Ideal Contexts to Issue Reminders for Persons with Dementia, IWAAL (2014), pp. 369-376Cleland I., Nugent C.D., McClean S.I., Hartin P.J., Sanders C., Donnelly M., Tschanz J.T. Predicting Technology Adoption in People with Dementia; Initial Results from the TAUT Project Ambient Assisted Living and Daily Activities, Springer International Publishing Soft Computing for Hybrid Intelligent Systems (2014), pp. 266-274 2008Soft Computing for Hybrid Intelligent Systems Oscar Castillo, Patricia Melin, Janusz Kacprzyk, Witold Pedrycz (Eds.), Studies in Computational Intelligence, 154, Springer (2008) ISBN 978-3-540-70811-7Ariza-Colpas, P., Oviedo-Carrascal, A. I., & De-la-hoz-Franco, E. (2019, July). Using K-Means Algorithm for Description Analysis of Text in RSS News Format. In International Conference on Data Mining and Big Data (pp. 162–169). Springer, Singapore.Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M., De la Hoz-Franco, E., Echeverri-Ocampo, I., & Salas-Navarro, K. (2019, July). Parkinson Disease Analysis Using Supervised and Unsupervised Techniques. In International Conference on Swarm Intelligence (pp. 191–199). Springer, ChamCalabria-Sarmiento, J. C., Ariza-Colpas, P., Pineres-Melo, M., Ayala-Mantilla, C., Urina-Triana, M., Morales-Ortega, R., … & EcheverriOcampo, I. (2018). Software applications to health sector: a systematic review of literature.Piñeres-Melo, M. A., Ariza-Colpas, P. P., Nieto-Bernal, W., & Morales-Ortega, R. (2019, July). SSwWS: Structural Model of Information Architecture. In International Conference on Swarm Intelligence (pp. 400–410). Springer, Cham.Ho T.K. 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Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset

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