Supervised Learning Techniques for Stress Detection in Car Drivers

[1]  Progresses in Artificial Intelligence and Neural Systems , 2021, Smart Innovation, Systems and Technologies.

[2]  Riccardo Bernardini,et al.  Stress Evaluation in Simulated Autonomous and Manual Driving through the Analysis of Skin Potential Response and Electrocardiogram Signals , 2020, Sensors.

[3]  Riccardo Bernardini,et al.  Car Driver's Sympathetic Reaction Detection Through Electrodermal Activity and Electrocardiogram Measurements , 2020, IEEE Transactions on Biomedical Engineering.

[4]  Laurent Albera,et al.  Emotion Recognition Based on High-Resolution EEG Recordings and Reconstructed Brain Sources , 2020, IEEE Transactions on Affective Computing.

[5]  Ryan Wen Liu,et al.  A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM , 2020, Sensors.

[6]  Robert Stone,et al.  Affective Recognition in Dynamic and Interactive Virtual Environments , 2020, IEEE Transactions on Affective Computing.

[7]  Arvind Sridhar,et al.  DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data , 2019, Explainable AI in Healthcare and Medicine.

[8]  Zahid Halim,et al.  On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning , 2020, Inf. Fusion.

[9]  Andry Rakotonirainy,et al.  Automatic driver stress level classification using multimodal deep learning , 2019, Expert Syst. Appl..

[10]  Inchan Youn,et al.  Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network , 2019, Sensors.

[11]  Manolis Tsiknakis,et al.  A novel multi-kernel 1D convolutional neural network for stress recognition from ECG , 2019, 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW).

[12]  Riccardo Bernardini,et al.  Stress Detection Through Electrodermal Activity (EDA) and Electrocardiogram (ECG) Analysis in Car Drivers , 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

[13]  Shaun Canavan,et al.  Synthesizing Physiological and Motion Data for Stress and Meditation Detection , 2019, 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW).

[14]  O. Ben-Ari,et al.  Driven by emotions: The association between emotion regulation, forgivingness, and driving styles , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[15]  Sang-Goog Lee,et al.  An Evaluation Method of Safe Driving for Senior Adults Using ECG Signals , 2019, Sensors.

[16]  Kyandoghere Kyamakya,et al.  A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors , 2019, Sensors.

[17]  Pamela Zontone,et al.  Dual channel Electrodermal activity sensor for motion artifact removal in car drivers’ stress detection , 2019, 2019 IEEE Sensors Applications Symposium (SAS).

[18]  Riccardo Bernardini,et al.  Low-Complexity Classification Algorithm to Identify Drivers’ Stress Using Electrodermal Activity (EDA) Measurements , 2018 .

[19]  Mattia Bruschetta,et al.  Assisted / autonomous vs. human driving assessment on the DiM driving simulator using objective / subjective characterization , 2020 .

[20]  Riccardo Bernardini,et al.  Driver’s stress detection using Skin Potential Response signals , 2018, Measurement.

[21]  José Manuel Ferrández,et al.  EEG-Based Detection of Braking Intention Under Different Car Driving Conditions , 2018, Front. Neuroinform..

[22]  HwangBosun,et al.  Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals. , 2018 .

[23]  Chandran Saravanan,et al.  Advancements and recent trends in emotion recognition using facial image analysis and machine learning models , 2017, 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT).

[24]  Wanhui Wen,et al.  Recognition of Real-Scene Stress in Examination with Heart Rate Features , 2017, 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC).

[25]  Yihan Zhao,et al.  Stress level evaluation using BP Neural network based on time-frequency analysis of HRV , 2017, 2017 IEEE International Conference on Mechatronics and Automation (ICMA).

[26]  Hung T. Nguyen,et al.  A CHF detection method based on deep learning with RR intervals , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[27]  Federica Marcolin,et al.  Psychological Stress Detection by 2D and 3D Facial Image Processing , 2017, IIH-MSP.

[28]  Dae SeokLee,et al.  Stress Events Detection of Driver by Wearable Glove System , 2017, IEEE Sensors Journal.

[29]  Zhiping Lin,et al.  Investigation on driver stress utilizing ECG signals with on-board navigation systems in use , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[30]  Himanshu Thapliyal,et al.  A Survey of Affective Computing for Stress Detection: Evaluating technologies in stress detection for better health , 2016, IEEE Consumer Electronics Magazine.

[31]  A. Affanni.  Wearable instrument to measure simultaneously cardiac and electrodermal activities , 2016, 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[32]  Giovanni Chiorboli,et al.  Design and characterization of a real-time, wearable, endosomatic electrodermal system , 2015 .

[33]  Hajime Asama,et al.  Estimation of stress during car race with factor analysis , 2015, 2015 International Symposium on Micro-NanoMechatronics and Human Science (MHS).

[34]  Hiroshi Mizoguchi,et al.  Real-time eyelid open/closed state recognition based on HLAC towards driver drowsiness detection , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[35]  Mika P. Tarvainen,et al.  Kubios HRV - Heart rate variability analysis software , 2014, Comput. Methods Programs Biomed..

[36]  David D. Cox,et al.  Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms , 2013, SciPy.

[37]  Yorgos Goletsis,et al.  Real-Time Driver's Stress Event Detection , 2012, IEEE Transactions on Intelligent Transportation Systems.

[38]  W. Boucsein.  Electrodermal activity, 2nd ed. , 2012 .

[39]  Yuan-Pin Lin,et al.  EEG-Based Emotion Recognition in Music Listening , 2010, IEEE Transactions on Biomedical Engineering.

[40]  Alain Pruski,et al.  Emotion recognition for human-machine communication , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[41]  Christos D. Katsis,et al.  Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[42]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[43]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[44]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.