Time-elastic generative model for acceleration time series in human activity recognition

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dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Ruiz Blázquez, Ramona
dc.date.accessioned 2018-01-31T10:36:43Z
dc.date.available 2018-01-31T10:36:43Z
dc.date.issued 2017-02-06
dc.identifier.bibliographicCitation Munoz-Organero, M., Ruiz-Blazquez, R. (2017). Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition. Sensors, 17 (2), 319.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/26167
dc.description.abstract Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.
dc.description.sponsorship The research leading to these results received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO, and from the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R, also funded by the Spanish MINECO.
dc.format.extent 18
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI (Multidisciplinary Digital Publishing Institute)
dc.rights © 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
dc.rights CC0 1.0 Universal
dc.rights.uri http://creativecommons.org/publicdomain/zero/1.0/
dc.subject.other Accelerometer sensors
dc.subject.other Auto-encoders
dc.subject.other Generative models for taining deep learning algorithms
dc.subject.other Human Action Recognition
dc.title Time-elastic generative model for acceleration time series in human activity recognition
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3390/s17020319
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2013-46801-C4-2-R
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.type.version publishedVersion
dc.identifier.publicationissue 2 (319)
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 17
dc.identifier.uxxi AR/0000019676
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