Two Stress Detection Schemes Based on Physiological Signals for Real-Time Applications

Sierra, Alberto de Santos; Ávila, Carmen Sánchez; Casanova, Javier Guerra; Pozo, Gonzalo Bailador del; Vera, Vicente

doi:10.1109/iihmsp.2010.95

Cited by 24 publications

(7 citation statements)

References 16 publications

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“…It is an established physiological measure of stress [7]. Empirical research evidence has demonstrated that HR may increase during periods of stress [17][18][19]. Previous research has also shown that the heart rate was affected during both acute and chronic stress through complex patterns [7].…”

Section: B Physiological Measure Of Stressmentioning

confidence: 99%

Stress Pattern Recognition Through Wearable Biosensors in the Workplace: Experimental Longitudinal Study on the Role of Motion Intensity

Mozgovoy

2019

2019 6th Swiss Conference on Data Science (SDS)

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Stress is a current issue in the workplace, manifesting itself through both psychological and physiological reactions. Biosensors might improve stress monitoring in the workplace, when employees become wearable device users. Yet, it remains unclear how to identify stress patterns through biosensors without direct observation of the users' activities. In particular, non-physiological aspects of employee activities altering physiological reactions, such as motion activity, may also be associated with stress measures. This longitudinal experimental study examines remote stress identification by testing whether a non-physiological signal of physical activity may improve the classification of stress-related physiological data collected through biosensors. The participants are 18 employees from Public Administration sector wearing biometric devices for around two months in the workplace. This study investigates the stress-related data classification, using established physiological measures (Galvanic Skin Response and Heart Rate) combined with a new non-physiological measure, associated with the user's physical activity (Motion Activity). Stress-related patterns are explored through unsupervised learning approach with help of Gaussian Mixture Model and K-Means classification analysis, completed by the bootstrap confidence intervals for evaluating uncertainty of classification. The results demonstrate that complementing physiological signals with a non-physiological signal, such as a physical activity-related information, improves stress pattern recognition through detection of emotional overarousal, arousal, and relaxation. These findings are especially promising in the context of the use of wearable devices for stress management, when stress-monitoring is done remotely and user' activity is not directly observed during measurements. Further research and cross-validation procedures should be used for building stress-identification algorithms for remote stress monitoring that include physiological and nonphysiological signals. Better understanding of stress measures may enhance the quality of stress management data collection processes through Information Systems, involved in the use of wearable devices in the workplace, and strengthen the data governance.

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Section: B Physiological Measure Of Stressmentioning

confidence: 99%

Stress Pattern Recognition Through Wearable Biosensors in the Workplace: Experimental Longitudinal Study on the Role of Motion Intensity

Mozgovoy

2019

2019 6th Swiss Conference on Data Science (SDS)

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“…HR was measured in beats per minute (bpm) [25] and GSR was measured in kilo-ohms (kohms) [26,27]. GSR reflects electrical conductivity, which changes when the skin glands produce ionic sweat [28]. It is linked to the emotional arousal state, where higher levels may increase the GSR [26,27].…”

Section: Pre-processing Feature Extraction and Measurementsmentioning

confidence: 99%

Longitudinal estimation of stress-related states through bio-sensor data

Mozgovoy

2021

ACI

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PurposeThe authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.Design/methodology/approachThe authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.FindingsThe authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.Research limitations/implicationsProposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.Practical implicationsLongitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.Social implicationsTimely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.Originality/valueThe study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

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“…Many different signals have been successfully used in this area, including heart rate (HR), respiration rate (RR), pupillary diameter, skin temperature (ST), electrodermal activity (EDA, also known as GSR, or galvanic skin response), blood volume pulse, electrocardiogram (ECG), blood pressure, electromyogram, and others [6], [17], [11], [3], [7], [4], [12], [2]. The validity of these measurements for detecting stress has been shown in numerous studies.…”

Section: Literature Reviewmentioning

confidence: 99%