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RETRACTED ARTICLE: An efficient deep learning based stress monitoring model through wearable devices for health care applications
14 references·0 citing·47 second hop
RETRACTED ARTICLE: An efficient deep learning based stress monitoring model through wearable devices for health care applications (2023)RETRACTED ARTICLE: An efficient deep lear…Measurement of multimodal physiological signals for stimulation detection by wearable devices (2021)Measurement of multimodal phy…Prediction of stress levels with LSTM and passive mobile sensors (2019)Prediction of stress levels w…Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices (2019)Multi-Modal Acute Stress Reco…Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study (2019)Continuous Stress Detection U…Unobtrusive stress detection on the basis of smartphone usage data (2018)Unobtrusive stress detection …Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection (2018)Introducing WESAD, a Multimod…Monitoring stress with a wrist device using context (2017)Monitoring stress with a wris…Keep the Stress Away with SoDA: Stress Detection and Alleviation System (2017)Keep the Stress Away with SoD…Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers (2017)Detecting driving stress in p…Stress Detection Using Wearable Physiological and Sociometric Sensors (2016)Stress Detection Using Wearab…Stress and anxiety detection using facial cues from videos (2016)Stress and anxiety detection …Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step (2015)Automatic Stress Detection in…Objective measures, sensors and computational techniques for stress recognition and classification: A survey (2012)Objective measures, sensors a…Call Center Stress Recognition with Person-Specific Models (2011)Call Center Stress Recognitio…A power primer. 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(1988)Development and validation of brief measures of positive and negative affect: The PANAS scales. (1988)DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2011)A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques (2021)A systematic review on affective computing: emotion models, databases, and recent advances (2022)Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations (2023)cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing (2015)Detecting Moments of Stress from Measurements of Wearable Physiological Sensors (2019)Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review (2021)
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References

  • 2018Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection1,356 cited
  • 2012Objective measures, sensors and computational techniques for stress recognition and classification: A survey510 cited
  • 2017Monitoring stress with a wrist device using context353 cited
  • 2019Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study351 cited
  • 2016Stress and anxiety detection using facial cues from videos343 cited
  • 2015Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step266 cited
  • 2017Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers245 cited
  • 2011Call Center Stress Recognition with Person-Specific Models210 cited
  • 2016Stress Detection Using Wearable Physiological and Sociometric Sensors209 cited
  • 2017Keep the Stress Away with SoDA: Stress Detection and Alleviation System142 cited
  • 2019Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices61 cited
  • 2018Unobtrusive stress detection on the basis of smartphone usage data57 cited
  • 2021Measurement of multimodal physiological signals for stimulation detection by wearable devices48 cited
  • 2019Prediction of stress levels with LSTM and passive mobile sensors

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