Stress Detection in Working People
TLDR
An attempt is made to determine the best feature set that results in maximum classification accuracy and the result indicates feature vector with best features having a strong influence in stress identification.About:
This article is published in Procedia Computer Science.The article was published on 2017-01-01 and is currently open access. It has received 98 citations till now. The article focuses on the topics: Feature vector & Support vector machine.read more
Citations
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Journal ArticleDOI
Detecting Moments of Stress from Measurements of Wearable Physiological Sensors.
K. Kyriakou,Bernd Resch,Günther Sagl,Andreas Petutschnig,Christian Werner,David Niederseer,Michael Liedlgruber,Frank H. Wilhelm,Tess Osborne,Jessica Pykett +9 more
TL;DR: The presented research proposes a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies, and shows that the algorithm detects MOS with 84% accuracy.
Journal ArticleDOI
Self-supervised ECG Representation Learning for Emotion Recognition
Pritam Sarkar,Ali Etemad +1 more
TL;DR: In this article, a self-supervised deep multi-task learning framework for electrocardiogram (ECG)-based emotion recognition is proposed, which consists of two stages of learning a) learning ECG representations and b) learning to classify emotions.
Proceedings ArticleDOI
Self-Supervised Learning for ECG-Based Emotion Recognition
Pritam Sarkar,Ali Etemad +1 more
TL;DR: The proposed method outperforms the state-of-the-art in ECG-based emotion recognition with two publicly available datasets, SWELL and AMIGOS, and highlights the advantage of the self-supervised approach in requiring significantly less data to achieve acceptable results.
Journal ArticleDOI
Portable System for Real-Time Detection of Stress Level.
Jesus Minguillon,Eduardo Perez,M. A. Lopez-Gordo,Francisco J. Pelayo,Maria Jose Sanchez-Carrion +4 more
TL;DR: A portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response that can be used to prevent stress episodes in many situations of everyday life such as work, school, and home is proposed.
Journal ArticleDOI
Mental stress detection using bioradar respiratory signals
TL;DR: A new stress detection technique based on signals taken by a bioradar is presented, one of the first solutions based exclusively on respiratory signals, and the novelty of the research also lies in the use of Recurrence Quantification Analysis features on respiratory recordings.
References
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World Health Report
TL;DR: The launch of The World Health Report (WHR) 2013 in Beijing in September of this year brought the total of such reports to 16 since the World Health Organization (WHO) brought out the first edition in 1995.
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A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee
TL;DR: A stress sensor based on Galvanic Skin Response (GSR), and controlled by ZigBee is designed and built, and appreciated that GSR is able to detect the different states of each user with a success rate of 76.56%.
Proceedings ArticleDOI
Continuous stress detection using a wrist device: in laboratory and real life
TL;DR: A method for continuous detection of stressful events using data provided from a commercial wrist device that consists of three machine-learning components: a laboratory stress detector that detects short-term stress every 2 minutes; an activity recognizer that continuously recognizes user's activity and thus provides context information; and a context-based stress detectors that exploits the output of the laboratorystress detector and the user's context in order to provide the final decision on 20 minutes interval.
Proceedings ArticleDOI
The SWELL Knowledge Work Dataset for Stress and User Modeling Research
TL;DR: The new multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling is described, which contains raw data, but also preprocessed data and extracted features.
Journal ArticleDOI
Detecting Work Stress in Offices by Combining Unobtrusive Sensors
TL;DR: The focus of this paper is on developing automatic classifiers to infer working conditions and stress related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture and physiology).