Human movement recognition based on the stochastic characterisation of acceleration data

e-Archivo Repository

Show simple item record

dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Lotfi, Ahmad
dc.date.accessioned 2018-02-22T16:25:38Z
dc.date.available 2018-02-22T16:25:38Z
dc.date.issued 2016-09-09
dc.identifier.bibliographicCitation Munoz-Organero, M., Lotfi, A. (2016). Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data. Sensors, 16 (9), 1464.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/25390
dc.description.abstract Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented.
dc.description.sponsorship The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte
dc.format.extent 16
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI (Multidisciplinary Digital Publishing Institute)
dc.rights © 2016 by the authors; licensee MDPI, Basel, Switzerland.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Human movement detection
dc.subject.other Activities
dc.subject.other Wearable sensors
dc.subject.other Fall detection
dc.subject.other Motion
dc.title Human movement recognition based on the stochastic characterisation of acceleration data
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3390/s16091464
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2013-46801-C4-2-R
dc.relation.projectID Gobierno de España. PRX15/00036
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 9 (1464)
dc.identifier.publicationlastpage 16
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 16
dc.identifier.uxxi AR/0000018351
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record