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      Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices

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          Abstract

          Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.

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          A survey of mobile phone sensing

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            Multisensor data fusion: A review of the state-of-the-art

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              Smartphone-based recognition of states and state changes in bipolar disorder patients.

              Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                21 February 2018
                February 2018
                : 18
                : 2
                : 640
                Affiliations
                [1 ]Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal; ngarcia@ 123456di.ubi.pt (N.M.G.); ngpombo@ 123456ubi.pt (N.P.)
                [2 ]Altranportugal, 1990-096 Lisbon, Portugal
                [3 ]ALLab—Assisted Living Computing and Telecommunications Laboratory, Computing Science Department, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
                [4 ]ECATI, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal
                [5 ]Department of Computer Technology, Universidad de Alicante, 03690 Sant Vicent del Raspeig, Alicante, Spain; francisco.florez@ 123456ua.es
                [6 ]Department of Information Engineering, Marche Polytechnic University, 60121 Ancona, Italy; s.spinsante@ 123456univpm.it
                Author notes
                [* ]Correspondence: impires@ 123456it.ubi.pt ; Tel.: +351-966-379-785
                Author information
                https://orcid.org/0000-0002-3394-6762
                https://orcid.org/0000-0002-3195-3168
                https://orcid.org/0000-0002-3391-711X
                https://orcid.org/0000-0002-7323-4030
                Article
                sensors-18-00640
                10.3390/s18020640
                5855971
                29466316
                9335ac82-0116-4601-97e0-1b1fe835be16
                © 2018 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/).

                History
                : 07 January 2018
                : 19 February 2018
                Categories
                Article

                Biomedical engineering
                activities of daily living (adl),environment,sensors,mobile devices,framework,data acquisition,data processing,data fusion,pattern recognition,machine learning

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