Complementing real datasets with simulated data: a regression-based approach
Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs AD...
- Autores:
-
Ortiz Barrios, Miguel Angel
Lundstrom, J.
Synnott, J.
Jarpe, E.
Sant’Anna, A.
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7767
- Acceso en línea:
- https://hdl.handle.net/11323/7767
https://doi.org/10.1007/s11042-019-08368-5
https://repositorio.cuc.edu.co/
- Palabra clave:
- Activity recognition
Activity duration
Regression analysis
Non-linear models
Determination coefficient
Quantile-quantile plots
- Rights
- openAccess
- License
- CC0 1.0 Universal
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oai:repositorio.cuc.edu.co:11323/7767 |
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|
dc.title.spa.fl_str_mv |
Complementing real datasets with simulated data: a regression-based approach |
title |
Complementing real datasets with simulated data: a regression-based approach |
spellingShingle |
Complementing real datasets with simulated data: a regression-based approach Activity recognition Activity duration Regression analysis Non-linear models Determination coefficient Quantile-quantile plots |
title_short |
Complementing real datasets with simulated data: a regression-based approach |
title_full |
Complementing real datasets with simulated data: a regression-based approach |
title_fullStr |
Complementing real datasets with simulated data: a regression-based approach |
title_full_unstemmed |
Complementing real datasets with simulated data: a regression-based approach |
title_sort |
Complementing real datasets with simulated data: a regression-based approach |
dc.creator.fl_str_mv |
Ortiz Barrios, Miguel Angel Lundstrom, J. Synnott, J. Jarpe, E. Sant’Anna, A. |
dc.contributor.author.spa.fl_str_mv |
Ortiz Barrios, Miguel Angel Lundstrom, J. Synnott, J. Jarpe, E. Sant’Anna, A. |
dc.subject.spa.fl_str_mv |
Activity recognition Activity duration Regression analysis Non-linear models Determination coefficient Quantile-quantile plots |
topic |
Activity recognition Activity duration Regression analysis Non-linear models Determination coefficient Quantile-quantile plots |
description |
Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs ADLs in different ways and in homes with different layouts. One possible solution is to obtain large amounts of data to train a supervised classifier. Data collection in real environments, however, is very expensive and cannot contain every possible variation of how different ADLs are performed. A more cost-effective solution is to generate a variety of simulated scenarios and synthesize large amounts of data. Nonetheless, simulated data can be considerably different from real data. Therefore, this paper proposes the use of regression models to better approximate real observations based on simulated data. To achieve this, ADL data from a smart home were first compared with equivalent ADLs performed in a simulator. Such comparison was undertaken considering the number of events per activity, number of events per type of sensor per activity, and activity duration. Then, different regression models were assessed for calculating real data based on simulated data. The results evidenced that simulated data can be transformed with a prediction accuracy R2 = 97.03%. |
publishDate |
2019 |
dc.date.issued.none.fl_str_mv |
2019-10-09 |
dc.date.accessioned.none.fl_str_mv |
2021-01-27T14:33:38Z |
dc.date.available.none.fl_str_mv |
2021-01-27T14:33:38Z |
dc.type.spa.fl_str_mv |
Pre-Publicación |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_816b |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/preprint |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ARTOTR |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_816b |
status_str |
acceptedVersion |
dc.identifier.issn.spa.fl_str_mv |
1380-7501 1573-7721 |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/7767 |
dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1007/s11042-019-08368-5 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
1380-7501 1573-7721 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/7767 https://doi.org/10.1007/s11042-019-08368-5 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.source.spa.fl_str_mv |
Multimedia Tools and Applications |
institution |
Corporación Universidad de la Costa |
dc.source.url.spa.fl_str_mv |
https://link.springer.com/article/10.1007/s11042-019-08368-5 |
bitstream.url.fl_str_mv |
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Ortiz Barrios, Miguel AngelLundstrom, J.Synnott, J.Jarpe, E.Sant’Anna, A.2021-01-27T14:33:38Z2021-01-27T14:33:38Z2019-10-091380-75011573-7721https://hdl.handle.net/11323/7767https://doi.org/10.1007/s11042-019-08368-5Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Activity recognition in smart environments is essential for ensuring the wellbeing of older residents. By tracking activities of daily living (ADLs), a person’s health status can be monitored over time. Nonetheless, accurate activity classification must overcome the fact that each person performs ADLs in different ways and in homes with different layouts. One possible solution is to obtain large amounts of data to train a supervised classifier. Data collection in real environments, however, is very expensive and cannot contain every possible variation of how different ADLs are performed. A more cost-effective solution is to generate a variety of simulated scenarios and synthesize large amounts of data. Nonetheless, simulated data can be considerably different from real data. Therefore, this paper proposes the use of regression models to better approximate real observations based on simulated data. To achieve this, ADL data from a smart home were first compared with equivalent ADLs performed in a simulator. Such comparison was undertaken considering the number of events per activity, number of events per type of sensor per activity, and activity duration. Then, different regression models were assessed for calculating real data based on simulated data. The results evidenced that simulated data can be transformed with a prediction accuracy R2 = 97.03%.Ortiz Barrios, Miguel Angel-will be generated-orcid-0000-0001-6890-7547-600Lundstrom, J.Synnott, J.Jarpe, E.Sant’Anna, A.application/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_abf2Multimedia Tools and Applicationshttps://link.springer.com/article/10.1007/s11042-019-08368-5Activity recognitionActivity durationRegression analysisNon-linear modelsDetermination coefficientQuantile-quantile plotsComplementing real datasets with simulated data: a regression-based approachPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionPublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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