Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs

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dc.contributor.author Muñoz Organero, Mario
dc.date.accessioned 2021-01-27T13:01:17Z
dc.date.available 2021-01-27T13:01:17Z
dc.date.issued 2019-06-05
dc.identifier.bibliographicCitation M. Munoz-Organero, "Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs," in IEEE Access, vol. 7, pp. 74422-74436, 2019
dc.identifier.issn 2169-3536
dc.identifier.uri http://hdl.handle.net/10016/31798
dc.description.abstract Wearable sensors provide a user-friendly and non-intrusive mechanism to extract user-relateddata that paves the way to the development of personalized applications. Within those applications, humanactivity recognition (HAR) plays an important role in the characterization of the user context. Outlierdetection methods focus on finding anomalous data samples that are likely to have been generated by adifferent mechanism. This paper combines outlier detection and HAR by introducing a novel algorithmthat is able both to detect information from secondary activities inside the main activity and to extract datasegments of a particular sub-activity from a different activity. Several machine learning algorithms havebeen previously used in the area of HAR based on the analysis of the time sequences generated by wearablesensors. Deep recurrent neural networks (DRNNs) have proven to be optimally adapted to the sequentialcharacteristics of wearable sensor data in previous studies. A DRNN-based algorithm is proposed in thispaper for outlier detection in HAR. The results are validated both for intra- and inter-subject cases and bothfor outlier detection and sub-activity recognition using two different datasets. A first dataset comprising4 major activities (walking, running, climbing up, and down) from 15 users is used to train and validatethe proposal. Intra-subject outlier detection is able to detect all major outliers in the walking activity in thisdataset, while inter-subject outlier detection only fails for one participant executing the activity in a peculiarway. Sub-activity detection has been validated by finding out and extracting walking segments present inthe other three activities in this dataset. A second dataset using four different users, a different setting anddifferent sensor devices is used to assess the generalization of results.
dc.description.sponsorship This work was supported by the ‘‘ANALYTICS USING SENSOR DATA FOR FLATCITY’’ Project (MINECO/ ERDF, EU) funded in partby the Spanish Agencia Estatal de Investigación (AEI) under Grant TIN2016-77158-C4-1-R and in part by the European RegionalDevelopment Fund (ERDF).
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2019 IEEE
dc.subject.other Human activity recognition
dc.subject.other Wearable sensors
dc.subject.other Outlier detection
dc.subject.other Machine learning
dc.subject.other Deeplearning
dc.subject.other Recurrent neural networks
dc.subject.other LSTMs
dc.title Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs
dc.type research article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://dx.doi.org/10.1109/ACCESS.2019.2921096
dc.rights.accessRights open access
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.identifier.publicationfirstpage 74422
dc.identifier.publicationlastpage 74436
dc.identifier.publicationtitle IEEE Access
dc.identifier.publicationvolume 7
dc.identifier.uxxi AR/0000025017
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Ingeniería Telemática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)
dc.type.hasVersion AM
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