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Using smart offices to predict occupational stress
16 references·1 citing·45 second hop
Using smart offices to predict occupational stress (2018)Using smart offices to predict occupation…Detecting Work Stress in Offices by Combining Unobtrusive Sensors (2016)Detecting Work Stress in Offi…Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2015)Towards an automatic early st…The SWELL Knowledge Work Dataset for Stress and User Modeling Research (2014)The SWELL Knowledge Work Data…Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep (2013)Towards Measuring Stress with…Stress Recognition Using Wearable Sensors and Mobile Phones (2013)Stress Recognition Using Wear…Stress detection from speech and Galvanic Skin Response signals (2013)Stress detection from speech …A review of emotion recognition methods based on keystroke dynamics and mouse movements (2013)A review of emotion recogniti…2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (2013)2013 Humaine Association Conf…StressSense (2012)StressSenseObjective measures, sensors and computational techniques for stress recognition and classification: A survey (2012)Objective measures, sensors a…AffectAura (2012)AffectAuraAutomated stress detection using keystroke and linguistic features: An exploratory study (2009)Automated stress detection us…Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables (2006)Stress Detection in Computer …A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network (2006)A Real-Time Human Stress Moni…Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research (1988)Development of NASA-TLX (Task…A method of assessing change in a single subject: An alteration of the RC index (1986)A method of assessing change …A systematic hybrid machine learning approach for stress prediction (2023)A systematic hybrid machine l…Affective Computing (1997)Pupil size variation as an indication of affective processing (2003)Detecting Stress During Real-World Driving Tasks Using Physiological Sensors (2005)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)Psychotherapy outcome research: Methods for reporting variability and evaluating clinical significance (1984)Cognitive behavioral group treatment for social phobia: Comparison with a credible placebo control (1990)The contribution of reliable and clinically significant change methods to evidence-based mental health (1998)A taxonomy for responsiveness (2001)The social readjustment rating scale (1967)A Global Measure of Perceived Stress (1983)The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research (1989)Stress detection in daily life scenarios using smart phones and wearable sensors: A survey (2019)A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques (2021)Measuring emotion: The self-assessment manikin and the semantic differential (1994)The job demands-resources model of burnout. 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(1997)The Massachusetts Institute of Technology (1999)Smart technologies for long-term stress monitoring at work (2013)Towards Personal Stress Informatics: Comparing Minimally Invasive Techniques for Measuring Daily Stress in the Wild (2014)Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection (2020)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)Judgment under Uncertainty: Heuristics and Biases (1974)Judgment under Uncertainty: Heuristics and Biases (1975)Telling more than we can know: Verbal reports on mental processes. (1977)The Out-of-the-Loop Performance Problem and Level of Control in Automation (1995)Nasa-Task Load Index (NASA-TLX); 20 Years Later (2006)Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness (2012)A circumplex model of affect. (1980)Stress, appraisal, and coping (1985)Stress: Appraisal and Coping (2013)
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References

  • 1988Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research15,045 cited
  • 2013Stress Recognition Using Wearable Sensors and Mobile Phones523 cited
  • 2012StressSense511 cited
  • 2012Objective measures, sensors and computational techniques for stress recognition and classification: A survey510 cited
  • 1986A method of assessing change in a single subject: An alteration of the RC index509 cited
  • 2015Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review476 cited
  • 2013Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep302 cited
  • 2014The SWELL Knowledge Work Dataset for Stress and User Modeling Research292 cited
  • 2006Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables290 cited
  • 2012AffectAura242 cited
  • 2013Stress detection from speech and Galvanic Skin Response signals227 cited
  • 2009Automated stress detection using keystroke and linguistic features: An exploratory study221 cited
  • 2016Detecting Work Stress in Offices by Combining Unobtrusive Sensors184 cited
  • 2006A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network171 cited

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  • 20132013 Humaine Association Conference on Affective Computing and Intelligent Interaction163 cited
  • 2013A review of emotion recognition methods based on keystroke dynamics and mouse movements130 cited
  • Cited by

    • 2023A systematic hybrid machine learning approach for stress prediction45 cited