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      EMERALD—Exercise Monitoring Emotional Assistant

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          Abstract

          The increase in the elderly population in today’s society entails the need for new policies to maintain an adequate level of care without excessively increasing social spending. One of the possible options is to promote home care for the elderly. In this sense, this paper introduces a personal assistant designed to help elderly people in their activities of daily living. This system, called EMERALD, is comprised of a sensing platform and different mechanisms for emotion detection and decision-making that combined produces a cognitive assistant that engages users in Active Aging. The contribution of the paper is twofold—on the one hand, the integration of low-cost sensors that among other characteristics allows for detecting the emotional state of the user at an affordable cost; on the other hand, an automatic activity suggestion module that engages the users, mainly oriented to the elderly, in a healthy lifestyle. Moreover, by continuously correcting the system using the on-line monitoring carried out through the sensors integrated in the system, the system is personalized, and, in broad terms, emotionally intelligent. A functional prototype is being currently tested in a daycare centre in the northern area of Portugal where preliminary tests show positive results.

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          Most cited references46

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          Toward machine emotional intelligence: analysis of affective physiological state

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            Multimodal Emotion Recognition in Response to Videos

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              Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques.

              Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                25 April 2019
                April 2019
                : 19
                : 8
                : 1953
                Affiliations
                [1 ]Departamento de Sistemas Informaticos y Computación, Universitat Politècnica de València, Valencia 46022, Spain; jrincon@ 123456dsic.upv.es (J.A.R.); carrasco@ 123456dsic.upv.es (C.C.)
                [2 ]ALGORITMI Center/Department of Informatics, University of Minho, Braga 4704-553, Portugal; acosta@ 123456di.uminho.pt (A.C.); pjon@ 123456di.uminho.pt (P.N.)
                Author notes
                [* ]Correspondence: vinglada@ 123456dsic.upv.es
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-1153-0616
                https://orcid.org/0000-0002-6170-4912
                https://orcid.org/0000-0003-3649-6530
                https://orcid.org/0000-0002-2743-6037
                Article
                sensors-19-01953
                10.3390/s19081953
                6515366
                31027296
                73a0dddf-977b-4dd0-b10c-246b8c37b619
                © 2019 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
                : 20 March 2019
                : 22 April 2019
                Categories
                Article

                Biomedical engineering
                cognitive assistant,wearable,emotion detection,signal processing,elderly well-being

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