Classification of stress recognition using Artificial Neural Network

Alić, Berina; Sejdinović, Dijana; Gurbeta, Lejla; Badnjević, Almir

doi:10.1109/meco.2016.7525765

Cited by 46 publications

(11 citation statements)

References 3 publications

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“…Our results demonstrate the reliability of our system in the detection of three levels of stress with a resolution of a few seconds. Still, the results could improve by extracting more features (i.e., stress markers) from biosignals [ 38 ] and by using more powerful classifiers such as artificial neural networks [ 52 , 53 ]. For the aim of this work, biosignals were processed offline.…”

Section: Discussionmentioning

confidence: 99%

Portable System for Real-Time Detection of Stress Level

Minguillón

Pérez

López-Gordo

et al. 2018

Sensors

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Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.

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Section: Discussionmentioning

confidence: 99%

Portable System for Real-Time Detection of Stress Level

Minguillón

Pérez

López-Gordo

et al. 2018

Sensors

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“…Typical stimuli have involved the Trier Social Stress Test [29], [30], Stroop test [31], video stimuli, or arithmetic tasks [32]. Afterward, a variety of features have been extracted and then used as input of a machine learning model, e.g., support vector machines [30], [33], random forest [33], neural networks [34], Bayesian networks [35] or fuzzy logic [22], [36] to automatically discriminate stress states. Of note, only a few studies have shown a classification of multiple levels of stress, e.g., [23], [24], [33], usually related to the intensity of a single kind of stressor.…”

Section: Introductionmentioning

confidence: 99%

Acute Stress State Classification Based on Electrodermal Activity Modeling

Greco

Valenza

Lázaro

et al. 2023

IEEE Trans. Affective Comput.

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Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a singlesubject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.

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“…GSR is considered, in fact, one of the best predictors for this condition. Stress is how the human body responds to any kind of question or threat, releasing specific stress hormones, including adrenaline and cortisol, which awaken the body to "face" an emergency action (Alić et al, 2016). Among the tangible responses, which the body can put into action, there is a more accelerated heartbeat, contracting muscles, increased blood pressure, accelerated breathing and senses that become more acute (Smith et al, 2013).…”

Section: Epidermic Monitoringmentioning

confidence: 99%

ATTICUS: Ambient-Intelligent Tele-monitoring and Telemetry for Incepting and Catering Over hUman Sustainability

et al. 2021

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Wearable devices as medical technologies are becoming an integral part of our lives. Many research studies are dedicated to these devices and are mainly focused on providing personal analytics, measuring physical status, and acquiring physiological signals and parameters. These continuously evolving technologies play an important role in telemedicine. Telemedicine can be broadly defined as the use of advanced telecommunications technologies to support many medical activities, such as the diagnosis, the analysis of patient data, the improvement of disease management and the treatment in remote areas. In this article, we present ATTICUS (Ambient-intelligent Tele-monitoring and Telemetry for Incepting and Catering over hUman Sustainability), an innovative remote monitoring system for ambient-assisted living based on the analysis of vital and behavioral parameters. The ATTICUS system consists of two essential components: a smart wearable—in the form of a short singlet—made of innovative textile which allows the acquisition of real-time body signals, e.g., electrocardiogram (ECG), breathing wave, temperature, and a multi-level Decision Support System (DSS), a distributed software which integrates advanced machine learning methods to automatically detect anomalies. ATTICUS is capable of operating in different application scenarios. Especially, the system will support in-home and out-home monitoring, personal check-ups, and specialized check-ups. Thus, the system will positively impact the canonical medical practices allowing simultaneous and continuous monitoring of a large number of people.

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Classification of stress recognition using Artificial Neural Network

Cited by 46 publications

References 3 publications

Portable System for Real-Time Detection of Stress Level

Portable System for Real-Time Detection of Stress Level

Acute Stress State Classification Based on Electrodermal Activity Modeling

ATTICUS: Ambient-Intelligent Tele-monitoring and Telemetry for Incepting and Catering Over hUman Sustainability

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