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

dc.contributor.authorDe la Hoz, Emirospa
dc.contributor.authorAriza Colpas, Paola Patriciaspa
dc.contributor.authorMedina Quero, Javierspa
dc.contributor.authorEspinilla, Macarenaspa
dc.date.accessioned2018-11-20T19:42:13Z
dc.date.available2018-11-20T19:42:13Z
dc.date.issued2018-09-22
dc.identifier.issn21693536spa
dc.identifier.urihttp://hdl.handle.net/11323/1478spa
dc.description.abstractThe 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.spa
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.spa
dc.rightsAtribución – No comercial – Compartir igualspa
dc.subjectAmbient assisted living–AALeng
dc.subjecthuman activity recognition–HAReng
dc.subjectactivities of daily living–ADLeng
dc.subjectactivity recognition systems–ARSeng
dc.subjectdataseteng
dc.titleSensor-based datasets for human activity recognition - a systematic review of literatureeng
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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