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Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms
15 references·2 citing·47 second hop
Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms (2019)Fabrication of a portable device for stre…Portable System for Real-Time Detection of Stress Level (2018)Portable System for Real-Time…Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2015)Towards an automatic early st…Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations (2013)Wearable Physiological Sensor…Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep (2013)Towards Measuring Stress with…Stress detection from speech and Galvanic Skin Response signals (2013)Stress detection from speech …Smart technologies for long-term stress monitoring at work (2013)Smart technologies for long-t…A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals (2013)A comparative evaluation of n…StressSense (2012)StressSenseAffectAura (2012)AffectAuraMonitoring of mental workload levels during an everyday life office-work scenario (2011)Monitoring of mental workload…Facial expression analysis for predicting unsafe driving behavior (2010)Facial expression analysis fo…Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables (2006)Stress Detection in Computer …Multimodal affect recognition in learning environments (2005)Multimodal affect recognition…Detecting Stress During Real-World Driving Tasks Using Physiological Sensors (2005)Detecting Stress During Real-…Bodily Changes in Pain, Hunger, Fear, and Rage. (1917)Bodily Changes in Pain, Hunge…Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review (2025)Fusing Wearable Biosensors wi…Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli (2021)Correlation Analysis of Diffe…Affective Computing (1997)Pupil size variation as an indication of affective processing (2003)The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition (2011)Activity-Aware Mental Stress Detection Using Physiological Sensors (2012)Large-scale wearable data reveal digital phenotypes for daily-life stress detection (2018)Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research (1988)Improving fluid intelligence with training on working memory (2008)Discriminating Stress From Cognitive Load Using a Wearable EDA Device (2009)Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study (2015)Detecting Work Stress in Offices by Combining Unobtrusive Sensors (2016)Human Mental Workload: A Survey and a Novel Inclusive Definition (2022)Toward machine emotional intelligence: analysis of affective physiological state (2001)Toward an affect-sensitive multimodal human-computer interaction (2003)Empirically building and evaluating a probabilistic model of user affect (2009)Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010)Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate (2016)A Global Measure of Perceived Stress (1983)Development and validation of brief measures of positive and negative affect: The PANAS scales. 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I: Assessing Workers’ Stress (2019)UBFC-Phys: A Multimodal Database For Psychophysiological Studies of Social Stress (2021)Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events (1981)Vocal affect expression: A review and a model for future research. (1986)Measuring stress: A guide for health and social scientists (1996)Mental health monitoring with multimodal sensing and machine learning: A survey (2018)Stress detection in daily life scenarios using smart phones and wearable sensors: A survey (2019)Coefficient Alpha and the Internal Structure of Tests (1951)Inferring psychological significance from physiological signals. (1990)A Multimodal Database for Affect Recognition and Implicit Tagging (2011)Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection (2018)Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review (2020)A circumplex model of affect. 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References

  • 2005Detecting Stress During Real-World Driving Tasks Using Physiological Sensors2,205 cited
  • 1917Bodily Changes in Pain, Hunger, Fear, and Rage.1,903 cited
  • 2012StressSense511 cited
  • 2015Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review476 cited
  • 2005Multimodal affect recognition in learning environments339 cited
  • 2013Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep302 cited
  • 2006Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables290 cited
  • 2012AffectAura242 cited
  • 2011Monitoring of mental workload levels during an everyday life office-work scenario233 cited
  • 2013Stress detection from speech and Galvanic Skin Response signals227 cited
  • 2013A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals194 cited
  • 2013Smart technologies for long-term stress monitoring at work154 cited
  • 2018Portable System for Real-Time Detection of Stress Level98 cited
  • 2010Facial expression analysis for predicting unsafe driving behavior90 cited
  • 2013

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Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations
83 cited

Cited by

  • 2025Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review71 cited
  • 2021Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli54 cited