A framework for operator – workstation interaction in Industry 4.0
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[1] Xiaoping Chen,et al. Recognizing slow eye movement for driver fatigue detection with machine learning approach , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[2] Kyung S. Park. Human Reliability: Analysis, Prediction, and Prevention of Human Errors , 1986 .
[3] Ron Van Houten,et al. Automated measurement in applied behavior analysis: A review , 2013 .
[4] Lifeng Zhou,et al. Industry 4.0: Towards future industrial opportunities and challenges , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
[5] Sergio Salmeron-Majadas,et al. Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts , 2015, AIED.
[6] Zhu Wang,et al. Enabling non-invasive and real-time human-machine interactions based on wireless sensing and fog computing , 2018, Personal and Ubiquitous Computing.
[7] Barry Strauch,et al. Investigating Human Error: Incidents, Accidents, and Complex Systems , 2002 .
[8] Freimut Bodendorf,et al. Investigating Flexibility as a Performance Dimension of a Manufacturing Value Modeling Methodology (MVMM): A Framework for Identifying Flexibility Types in Manufacturing Systems , 2017 .
[9] Guang-Zhong Yang,et al. The use of pervasive sensing for behaviour profiling - a survey , 2009, Pervasive Mob. Comput..
[10] K. Stecke,et al. The evolution of production systems from Industry 2.0 through Industry 4.0 , 2018, Int. J. Prod. Res..
[11] CambriaErik,et al. A review of affective computing , 2017 .
[12] Yang Xiao,et al. Bio-inspired visual attention in agile sensing for target detection , 2009, Int. J. Sens. Networks.
[13] Gang Li,et al. Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier , 2013, Sensors.
[14] Yaoyao Fiona Zhao,et al. Enabling cognitive manufacturing through automated on-machine measurement planning and feedback , 2010, Adv. Eng. Informatics.
[15] Erik Cambria,et al. A review of affective computing: From unimodal analysis to multimodal fusion , 2017, Inf. Fusion.
[16] Satoshi Nakamura,et al. Lip movement synthesis from speech based on hidden Markov models , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.
[17] WöllmerMartin,et al. Emotion on the road , 2010 .
[18] Jennifer Healey,et al. Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Erik Cambria,et al. Affective Computing and Sentiment Analysis , 2016, IEEE Intelligent Systems.
[20] Mathias Schmitt,et al. Human-machine-interaction in the industry 4.0 era , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).
[21] C. Büchel,et al. Temporal and Spatial Dynamics of Brain Structure Changes during Extensive Learning , 2006, The Journal of Neuroscience.
[22] M. Eysenck. Attention And Arousal, Cognition And Performance , 1982 .
[23] Qijie Zhao,et al. Eye moving behaviors identification for gaze tracking interaction , 2014, Journal on Multimodal User Interfaces.
[24] Mona Omidyeganeh,et al. Intelligent driver drowsiness detection through fusion of yawning and eye closure , 2011, 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Proceedings.
[25] Manfred Hild,et al. Informatics for cognitive robots , 2010, Adv. Eng. Informatics.
[26] Víctor Peláez,et al. An automatic data mining method to detect abnormal human behaviour using physical activity measurements , 2014, Pervasive Mob. Comput..
[27] Bin Guo,et al. WhozDriving: Abnormal Driving Trajectory Detection by Studying Multi-faceted Driving Behavior Features , 2016, BigCom.
[28] Stavros J. Perantonis,et al. Detecting abnormal human behaviour using multiple cameras , 2009, Signal Process..
[29] Roberto Brunelli,et al. Person identification using multiple cues , 1995, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] J. Malouff,et al. Increasing Emotional Intelligence through Training: Current Status and Future Directions , 2013 .
[31] Minjuan Wang,et al. Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment , 2009, J. Educ. Technol. Soc..
[32] A. Jenness,et al. The recognition of facial expressions of emotion. , 1932 .
[33] E. Walker,et al. Diagnostic and Statistical Manual of Mental Disorders , 2013 .
[34] Minoru Asada,et al. Efficient human-robot collaboration: When should a robot take initiative? , 2017, Int. J. Robotics Res..
[35] Michael J. Lyons,et al. Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.
[36] Adam M. Grant. Does intrinsic motivation fuel the prosocial fire? Motivational synergy in predicting persistence, performance, and productivity. , 2008, The Journal of applied psychology.
[37] Aurobinda Routray,et al. Automatic facial expression recognition using features of salient facial patches , 2015, IEEE Transactions on Affective Computing.
[38] F. Chan,et al. The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda , 2018, Int. J. Prod. Res..
[39] Peter Xiaoping Liu,et al. Visual gesture recognition for human-machine interface of robot teleoperation , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).
[40] Digitalization of Manufacturing Execution Systems : the core technology for realizing future Smart Factories , 2017 .
[41] Frank Dellaert,et al. Recognizing emotion in speech , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.
[42] Gordon D Logan,et al. Journal of Experimental Psychology : General Prevention and Correction in Post-Error Performance : An Ounce of Prevention , a Pound of Cure , 2012 .
[43] Luis Miguel Bergasa,et al. DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.
[44] Phillip L. Ackerman,et al. Individual differences in work motivation: Further explorations of a trait framework. , 2000 .
[45] Christian Montag,et al. Primal emotional-affective expressive foundations of human facial expression , 2016 .
[46] Stanley B. Gershwin,et al. The future of manufacturing systems engineering , 2018, Int. J. Prod. Res..
[47] Björn W. Schuller,et al. Emotion on the Road - Necessity, Acceptance, and Feasibility of Affective Computing in the Car , 2010, Adv. Hum. Comput. Interact..
[48] P. de Jonge,et al. Detecting mental disorders in general hospitals by the SCL-8 scale. , 2004, Journal of psychosomatic research.
[49] J. M. O. Pinto,et al. Human-Machine Interface (HMI) scenario quantification performed by ATHEANA, A Technique for Human Error Analysis , 2015 .
[50] Lauri Nummenmaa,et al. Recognition of Facial Expressions of Emotion is Related to their Frequency in Everyday Life , 2014, Journal of Nonverbal Behavior.
[51] Álvaro Segura,et al. Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm , 2019, Int. J. Prod. Res..
[52] Cain C T Clark,et al. A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans , 2016, Sports Medicine.
[53] Carsten Wittenberg,et al. Human-CPS Interaction - requirements and human-machine interaction methods for the Industry 4.0 , 2016 .
[54] Carlo Noe,et al. Literature review on the ‘Smart Factory’ concept using bibliometric tools , 2017, Int. J. Prod. Res..
[55] Michele Lora,et al. Validation of HMI applications for industrial smart display , 2017, 2017 IEEE International High Level Design Validation and Test Workshop (HLDVT).
[56] P D Bamidis,et al. Affective Medicine , 2010, Methods of Information in Medicine.
[57] Arkady B. Zaslavsky,et al. Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.
[58] Anupam Agrawal,et al. Vision based hand gesture recognition for human computer interaction: a survey , 2012, Artificial Intelligence Review.
[59] Roberto Revetria,et al. Augmented and virtual reality applications in industrial systems: A qualitative review towards the industry 4.0 era , 2018 .
[60] José L. Martínez Lastra,et al. From artificial cognitive systems and open architectures to cognitive manufacturing systems , 2015, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN).
[61] Conrad S. Tucker,et al. Machine learning classification of design team members' body language patterns for real time emotional state detection , 2015 .
[62] Qinghua Hu,et al. Kernelized Fuzzy Rough Sets Based Yawn Detection for Driver Fatigue Monitoring , 2011, Fundam. Informaticae.
[63] Tieniu Tan,et al. Affective Computing: A Review , 2005, ACII.
[64] Christopher D. Wickens,et al. Stages and Levels of Automation: An Integrated Meta-analysis , 2010 .
[65] L. Murukesan,et al. Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features , 2014 .
[66] Johan Stahre,et al. TOWARDS AN OPERATOR 4.0 TYPOLOGY: A HUMAN-CENTRIC PERSPECTIVE ON THE FOURTH INDUSTRIAL REVOLUTION TECHNOLOGIES , 2016 .
[67] Gangfeng Tan,et al. Driving Fatigue Detection based on Blink Frequency and Eyes Movement , 2017 .
[68] M. Carter. Diagnostic and Statistical Manual of Mental Disorders, 5th ed. , 2014 .