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Machine learning approach applied to human activity recognition – an application to the VanKasteren dataset
dc.contributor.author | Ariza Colpas, Paola Patricia | spa |
dc.contributor.author | Oñate-Bowen, Alvaro Agustín | spa |
dc.contributor.author | Suarez-Brieva, Eydy del Carmen | spa |
dc.contributor.author | Oviedo Carrascal, Ana Isabel | spa |
dc.contributor.author | Urina Triana, Miguel | spa |
dc.contributor.author | Piñeres Melo, Marlon Alberto | spa |
dc.contributor.author | Butt Shariq, Aziz | spa |
dc.contributor.author | COLLAZOS MORALES, CARLOS ANDRES | spa |
dc.contributor.author | Ramayo González, Ramón Enrique | spa |
dc.date.accessioned | 2021-09-15T15:03:24Z | |
dc.date.available | 2021-09-15T15:03:24Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1877-0509 | spa |
dc.identifier.uri | https://hdl.handle.net/11323/8696 | spa |
dc.description.abstract | 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% | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | |
dc.publisher | Corporación Universidad de la Costa | spa |
dc.rights | CC0 1.0 Universal | spa |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | spa |
dc.source | Procedia Computer Science | spa |
dc.subject | Machine learning | spa |
dc.subject | HARADL | spa |
dc.subject | Human activity recognition | spa |
dc.subject | Activity daily living | spa |
dc.subject | VanKasteren dataset | spa |
dc.title | Machine learning approach applied to human activity recognition – an application to the VanKasteren dataset | spa |
dc.type | Artículo de revista | spa |
dc.source.url | https://www.sciencedirect.com/science/article/pii/S1877050921014733 | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.doi | https://doi.org/10.1016/j.procs.2021.07.070 | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
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dc.relation.references | [4] Oliver Zangwill Centre Neuropsychological Rehabilitation Accessed December 2014. NeuroPage. Online, http://www.neuropage.nhs.uk/, 2014. | spa |
dc.relation.references | [5] Barbara A Wilson, Jonathan J Evans, Hazel Emslie, and Vlastimil Malinek. Evaluation of neuropage: a new memory aid. Journal of Neurology, Neurosurgery & Psychiatry, 63(1):113–115, 1997. | spa |
dc.relation.references | [6] Barbara A Wilson, Helena Scott, Jonathan Evans, and Hazel Emslie. Preliminary report of a neuropage service within a health care system. NeuroRehabilitation, 18(1):3–8, 2003. | spa |
dc.relation.references | [7] Wilson, HC Emslie, K Quirk, and 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):477–482, 2001 | spa |
dc.relation.references | [8] Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L., & Seeley, W. W. (2012). Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron, 73(6), 1216-1227. | spa |
dc.relation.references | [9] 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. | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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