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      Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

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          Abstract

          The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

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          Most cited references 47

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          Detecting Stress During Real-World Driving Tasks Using Physiological Sensors

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            Epilepsy across the spectrum: promoting health and understanding. A summary of the Institute of Medicine report.

            Approximately 1 in 26 people will develop epilepsy at some point in their lives. Although epilepsy is one of the nation's most common neurological disorders, public understanding is limited. A complex spectrum of disorders, epilepsy affects an estimated 2.2 million people in the United States. Living with epilepsy is about more than just seizures; it is often defined in practical terms, such as challenges, uncertainties, and limitations in school, social situations, employment, driving, and independent living. People with epilepsy are also faced with health and community services that are fragmented, uncoordinated, and difficult to obtain. The Institute of Medicine's report (2012) [1], Epilepsy across the spectrum: promoting health and understanding, examines the public health dimensions of epilepsy with a focus on (a) public health surveillance and data collection and integration; (b) population and public health research; (c) health policy, health care, and human services; and (d) education for providers, people with epilepsy and their families, and the public. The report's recommendations range from the expansion of collaborative epilepsy surveillance efforts to the independent accreditation of epilepsy centers, to the coordination of public awareness efforts, and to the engagement of people with epilepsy and their families in education, dissemination, and advocacy activities. Given the current gaps in epilepsy knowledge, care, and education, there is an urgent need to take action-across multiple dimensions-to improve the lives of people with epilepsy and their families. The realistic, feasible, and action-oriented recommendations in this report can help enable short- and long-term improvements for people with epilepsy. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Objective measures, sensors and computational techniques for stress recognition and classification: a survey.

              Stress is a major growing concern in our day and age adversely impacting both individuals and society. Stress research has a wide range of benefits from improving personal operations, learning, and increasing work productivity to benefiting society - making it an interesting and socially beneficial area of research. This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress, a term we coin in the paper. Sensors that do not impede everyday activities that could be used by those who would like to monitor stress levels on a regular basis (e.g. vehicle drivers, patients with illnesses linked to stress) is the focus of the discussion. Computational techniques have the capacity to determine optimal sensor fusion and automate data analysis for stress recognition and classification. Several computational techniques have been developed to model stress based on techniques such as Bayesian networks, artificial neural networks, and support vector machines, which this survey investigates. The survey concludes with a summary and provides possible directions for further computational stress research.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                18 April 2019
                April 2019
                : 19
                : 8
                Affiliations
                Department of Computer Engineering, Boğaziçi University, Bebek, Istanbul 34342, Turkey; niaz.chalabianloo@ 123456boun.edu.tr (N.C.); deniz.ekiz@ 123456boun.edu.tr (D.E.); ersoy@ 123456boun.edu.tr (C.E.)
                Author notes
                [* ]Correspondence: yekta.can@ 123456boun.edu.tr
                Article
                sensors-19-01849
                10.3390/s19081849
                6515276
                31003456
                © 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/).

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