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...
- Autores:
-
Ariza Colpas, Paola Patricia
Oñate-Bowen, Alvaro Agustín
Suarez-Brieva, Eydy del Carmen
Oviedo Carrascal, Ana Isabel
Urina Triana, Miguel
Piñeres Melo, Marlon Alberto
Butt Shariq, Aziz
COLLAZOS MORALES, CARLOS ANDRES
Ramayo González, Ramón Enrique
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8696
- Acceso en línea:
- https://hdl.handle.net/11323/8696
https://doi.org/10.1016/j.procs.2021.07.070
https://repositorio.cuc.edu.co/
- Palabra clave:
- Machine learning
HARADL
Human activity recognition
Activity daily living
VanKasteren dataset
- Rights
- openAccess
- License
- CC0 1.0 Universal
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RCUC2 |
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|
| dc.title.spa.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 HARADL 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 Patricia Oñate-Bowen, Alvaro Agustín Suarez-Brieva, Eydy del Carmen Oviedo Carrascal, Ana Isabel Urina Triana, Miguel Piñeres Melo, Marlon Alberto Butt Shariq, Aziz COLLAZOS MORALES, CARLOS ANDRES Ramayo González, Ramón Enrique |
| dc.contributor.author.spa.fl_str_mv |
Ariza Colpas, Paola Patricia Oñate-Bowen, Alvaro Agustín Suarez-Brieva, Eydy del Carmen Oviedo Carrascal, Ana Isabel Urina Triana, Miguel Piñeres Melo, Marlon Alberto Butt Shariq, Aziz COLLAZOS MORALES, CARLOS ANDRES Ramayo González, Ramón Enrique |
| dc.subject.spa.fl_str_mv |
Machine learning HARADL Human activity recognition Activity daily living VanKasteren dataset |
| topic |
Machine learning HARADL 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 |
2021-09-15T15:03:24Z |
| dc.date.available.none.fl_str_mv |
2021-09-15T15:03:24Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
1877-0509 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/8696 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1016/j.procs.2021.07.070 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/8696 https://doi.org/10.1016/j.procs.2021.07.070 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] Sohn, 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. [2] 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. [3] Pollock, P. M., Harper, U. L., Hansen, K. S., Yudt, L. M., Stark, M., Robbins, C. M., ... & Salem, G. (2003). High frequency of BRAF mutations in nevi. Nature genetics, 33(1), 19-20. [4] Oliver Zangwill Centre Neuropsychological Rehabilitation Accessed December 2014. NeuroPage. Online, http://www.neuropage.nhs.uk/, 2014. [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. [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. [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 [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. [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. |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
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http://creativecommons.org/publicdomain/zero/1.0/ |
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CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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Corporación Universidad de la Costa |
| dc.source.spa.fl_str_mv |
Procedia Computer Science |
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Corporación Universidad de la Costa |
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Ariza Colpas, Paola PatriciaOñate-Bowen, Alvaro AgustínSuarez-Brieva, Eydy del CarmenOviedo Carrascal, Ana IsabelUrina Triana, MiguelPiñeres Melo, Marlon AlbertoButt Shariq, AzizCOLLAZOS MORALES, CARLOS ANDRESRamayo González, Ramón Enrique2021-09-15T15:03:24Z2021-09-15T15:03:24Z20211877-0509https://hdl.handle.net/11323/8696https://doi.org/10.1016/j.procs.2021.07.070Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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%Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Oñate-Bowen, Alvaro AgustínSuarez-Brieva, Eydy del CarmenOviedo Carrascal, Ana Isabel-will be generated-orcid-0000-0002-7105-7819-600Urina Triana, MiguelPiñeres Melo, Marlon Alberto-will be generated-orcid-0000-0002-1858-2083-600Butt Shariq, AzizCOLLAZOS MORALES, CARLOS ANDRES-will be generated-orcid-0000-0002-1996-1384-600Ramayo González, Ramón Enrique-will be generated-orcid-0000-0001-6137-6181-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050921014733Machine learningHARADLHuman activity recognitionActivity daily livingVanKasteren datasetMachine learning approach applied to human activity recognition – an application to the VanKasteren datasetArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Sohn, 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.[2] 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.[3] Pollock, P. M., Harper, U. L., Hansen, K. S., Yudt, L. M., Stark, M., Robbins, C. M., ... & Salem, G. (2003). High frequency of BRAF mutations in nevi. Nature genetics, 33(1), 19-20.[4] Oliver Zangwill Centre Neuropsychological Rehabilitation Accessed December 2014. NeuroPage. Online, http://www.neuropage.nhs.uk/, 2014.[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.[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.[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[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.[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.PublicationORIGINALMachine learning approach applied to human activity recognition an application to the vankasteren dataset.pdfMachine learning approach applied to human activity recognition an application to the vankasteren dataset.pdfapplication/pdf831988https://repositorio.cuc.edu.co/bitstreams/bc31c67a-8c48-41be-9865-ecdbedf39ceb/downloadc1e5e514376c41eacf65786290aadaabMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/e5405a56-68fd-4cb5-ac68-85fbe50b8b83/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/cb03c4b9-56db-4128-8713-8667ca2a9745/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMachine learning approach applied to human activity recognition an application to the vankasteren dataset.pdf.jpgMachine learning approach applied to human activity recognition an 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