Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end use...

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Autores:
De la Hoz, Emiro
Ariza Colpas, Paola Patricia
Medina Quero, Javier
Espinilla, Macarena
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1478
Acceso en línea:
https://hdl.handle.net/11323/1478
https://repositorio.cuc.edu.co/
Palabra clave:
Ambient assisted living–AAL
human activity recognition–HAR
activities of daily living–ADL
activity recognition systems–ARS
dataset
Rights
openAccess
License
Atribución – No comercial – Compartir igual
id RCUC2_cfd57d5ee8373862d933f50c7851c2a9
oai_identifier_str oai:repositorio.cuc.edu.co:11323/1478
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Sensor-based datasets for human activity recognition - a systematic review of literature
title Sensor-based datasets for human activity recognition - a systematic review of literature
spellingShingle Sensor-based datasets for human activity recognition - a systematic review of literature
Ambient assisted living–AAL
human activity recognition–HAR
activities of daily living–ADL
activity recognition systems–ARS
dataset
title_short Sensor-based datasets for human activity recognition - a systematic review of literature
title_full Sensor-based datasets for human activity recognition - a systematic review of literature
title_fullStr Sensor-based datasets for human activity recognition - a systematic review of literature
title_full_unstemmed Sensor-based datasets for human activity recognition - a systematic review of literature
title_sort Sensor-based datasets for human activity recognition - a systematic review of literature
dc.creator.fl_str_mv De la Hoz, Emiro
Ariza Colpas, Paola Patricia
Medina Quero, Javier
Espinilla, Macarena
dc.contributor.author.spa.fl_str_mv De la Hoz, Emiro
Ariza Colpas, Paola Patricia
Medina Quero, Javier
Espinilla, Macarena
dc.subject.eng.fl_str_mv Ambient assisted living–AAL
human activity recognition–HAR
activities of daily living–ADL
activity recognition systems–ARS
dataset
topic Ambient assisted living–AAL
human activity recognition–HAR
activities of daily living–ADL
activity recognition systems–ARS
dataset
description The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2018-11-20T19:42:13Z
dc.date.available.none.fl_str_mv 2018-11-20T19:42:13Z
dc.date.issued.none.fl_str_mv 2018-09-22
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 21693536
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/1478
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 21693536
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/1478
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
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spelling De la Hoz, EmiroAriza Colpas, Paola PatriciaMedina Quero, JavierEspinilla, Macarena2018-11-20T19:42:13Z2018-11-20T19:42:13Z2018-09-2221693536https://hdl.handle.net/11323/1478Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.De la Hoz, Emiro-will be generated-orcid-0000-0002-4926-7414-600Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Medina Quero, Javier-will be generated-orcid-0000-0002-8577-8772-0Espinilla, Macarena-will be generated-orcid-0000-0003-1118-7782-0engInstitute of Electrical and Electronics Engineers Inc.Atribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ambient assisted living–AALhuman activity recognition–HARactivities of daily living–ADLactivity recognition systems–ARSdatasetSensor-based datasets for human activity recognition - a systematic review of literatureArtículo 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] D. 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Gjoreski, M. Gams, and M. Lustrek, ‘‘Context-based fall detection ˝ and activity recognition using inertial and location sensors,’’ J. Ambient Intell. Smart Environ., vol. 6, no. 4, pp. 419–433, 2014, doi: 10.3233/AIS140268. [18] H. H. Manap, N. M. Tahir, and A. I. M. Yassin, ‘‘Anomalous gait detection based on support vector machine,’’ in Proc. IEEE Int. Conf. Comput. Appl. Ind. Electron., Dec. 2011, pp. 623–626, doi: 10.1109/ ICCAIE.2011.6162209. [19] H. Gjoreski, B. Kaluža, M. Gams, M. Radoje, and M. Luštrek, ‘‘Context-based ensemble method for human energy expenditure estimation,’’ Appl. Soft Comput., vol. 37, pp. 960–970, Dec. 2015, doi: 10.1016/j.asoc.2015.05.001. [20] M. Altini, J. Penders, R. Vullers, and O. Amft, ‘‘Estimating energy expenditure using body-worn accelerometers: A comparison of methods, sensors number and positioning,’’ IEEE J. Biomed. Health Inform., vol. 19, no. 1, pp. 219–226, Jan. 2015, doi: 10.1109/JBHI.2014.2313039. [21] M. Gjoreski, H. Gjoreski, M. 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