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Modeling long-term human activeness using recurrent neural networks for biometric data

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BMC Medical Informatics and Decision Making

BioMed Central

The 6th Translational Bioinformatics Conference (TBC 2016)

15-17 October 2016

Heart rate, Calorie, Footstep, Activeness prediction, Time series modeling, Recurrent neural network

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      Abstract

      BackgroundWith the invention of fitness trackers, it has been possible to continuously monitor a user’s biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user’s “activeness”, and investigates the feasibility in modeling and predicting the long-term activeness of the user.MethodsThe dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures–as well as a deep neural network and a simple regression model–were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user’s activeness falls below a certain threshold.ResultsA preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user’s activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user’s future activeness with precision, for example, a trained RNN model could predict–with the precision of 84%–when the user would be less active within the next hour given the latest 15 min of his activeness data.ConclusionsThis paper defines and investigates the notion of a user’s “activeness”, and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.

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      Most cited references 18

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      Long Short-Term Memory

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        Learning long-term dependencies with gradient descent is difficult.

        Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered.
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            Author and article information

            Affiliations
            [1 ]ISNI 0000 0001 2292 0500, GRID grid.37172.30, School of Computing, , KAIST, ; 291 Daehak-ro, Yuseong-gu, Daejeon, 34141 South Korea
            [2 ]ISNI 0000 0001 1945 5898, GRID grid.419666.a, Samsung Seoul R&D Campus, , Samsung Electronics, ; 33 Seongchon-gil, Seocho-gu, Seoul, 06765 South Korea
            Contributors
            zaemyung@kaist.ac.kr
            hyungrai.oh@samsung.com
            kimhangyu@kaist.ac.kr
            rayote@kaist.ac.kr
            aomaru@kaist.ac.kr
            hojinc@kaist.ac.kr
            Conference
            BMC Med Inform Decis Mak
            BMC Med Inform Decis Mak
            BMC Medical Informatics and Decision Making
            BioMed Central (London )
            1472-6947
            18 May 2017
            18 May 2017
            2017
            : 17
            Issue : Suppl 1 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
            5444042
            453
            10.1186/s12911-017-0453-1
            © The Author(s) 2017

            Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

            The 6th Translational Bioinformatics Conference
            TBC 2016
            Je Ju Island, Korea
            15-17 October 2016
            Categories
            Research
            Custom metadata
            © The Author(s) 2017

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