Assessment of Human Fatigue during Physical Performance using Physiological Signals: A Review

Fatigue is a major safety concern for personnel in many fields which includes aviation & military personnel, astronauts, drivers, doctors, industrial workers, computer users, etc. Assessing human fatigue is essential for enhancing capability of the individual at work, thereby accomplishing more work efficiency and also ensuring the safety of individual. The survey reviews the various techniques that have been used to measure and recognize the fatigue in a person. Experts in research community have investigated human fatigue using various features of the physiological signals. The several physiological signals like Blood Volume Pulse (BVP), Electromyogram (EMG), Galvanic Skin Response (GSR), Electrocardiogram (ECG), and Respiratory signals are investigated to detect the stress and fatigue of a person. It also discusses various classification techniques that have been used by researchers to identify the fatigue in a person. This work presents a comprehensive survey of fatigue and stress detection using various physiological signals. Furthermore, it also aims to find the most appropriate features and techniques to efficiently assess human stress and fatigue.

[1]  Sazali Yaacob,et al.  EMG signal based human stress level classification using wavelet packet transform , 2012, ICRA 2012.

[2]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[3]  Marko Horvat,et al.  Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications , 2014, Int. J. Hum. Comput. Stud..

[4]  Martin L. Griss,et al.  Activity-Aware Mental Stress Detection Using Physiological Sensors , 2010, MobiCASE.

[5]  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.

[6]  Zhiwei Zhu,et al.  A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[7]  Venkatesh Balasubramanian,et al.  EEG based analysis of cognitive fatigue during simulated driving , 2011 .

[8]  Seyed Hamid Reza Abbasi,et al.  Emotions States Recognition Based on Physiological Parameters by Employing of Fuzzy-Adaptive Resonance Theory , 2012 .

[9]  S. Huffel,et al.  Influence of Mental Stress on Heart Rate and Heart Rate Variability , 2009 .

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

[11]  M. Subramanian,et al.  Psychological Stress Monitoring and Reporting System for Industries , 2013, 2013 Texas Instruments India Educators' Conference.

[12]  M. Murugappan,et al.  Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst , 2013, BioMedical Engineering OnLine.

[13]  Chuan-Yu Chang,et al.  Physiological emotion analysis using support vector regression , 2013, Neurocomputing.

[14]  Viera Stopjakova,et al.  Electrical biomonitoring towards mobile diagnostics of human stress influence , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.

[15]  Tatsuya Suzuki,et al.  Fatigue Recognition using EMG Signals and Stochastic Switched ARX Model , 2009, ICINCO-RA.

[16]  Armando Barreto,et al.  Stress detection in computer users through non-invasive monitoring of physiological signals. , 2006, Biomedical sciences instrumentation.

[17]  K. H. Kim,et al.  Emotion recognition system using short-term monitoring of physiological signals , 2004, Medical and Biological Engineering and Computing.

[18]  Mohammad Soleymani,et al.  A Multimodal Database for Affect Recognition and Implicit Tagging , 2012, IEEE Transactions on Affective Computing.

[19]  B. Geethanjali,et al.  Assessment of depression, anxiety and stress among female students , 2014 .

[20]  Gonzalo Bailador,et al.  A Stress-Detection System Based on Physiological Signals and Fuzzy Logic , 2011, IEEE Transactions on Industrial Electronics.

[21]  Sazali Yaacob,et al.  Multiple Physiological Signal-Based Human Stress Identification Using Non-Linear Classifiers , 2013 .

[22]  Javier Guerra-Casanova,et al.  Real-Time Stress Detection by Means of Physiological Signals , 2011 .

[23]  M Jagannath,et al.  Muscle fatigue based evaluation of bicycle design. , 2014, Applied ergonomics.

[24]  S. Gopalakrishnan A Public Health Perspective of Road Traffic Accidents , 2012, Journal of family medicine and primary care.

[25]  Chuan-Yu Chang,et al.  Physiological Angry Emotion Detection Using Support Vector Regression , 2012, 2012 15th International Conference on Network-Based Information Systems.

[26]  Yang Zhifa,et al.  An active driver fatigue identification technique using multiple physiological features , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[27]  Viera Stopjakova,et al.  Electro-Optical Monitoring and Analysis of Human Cognitive Processes , 2010 .

[28]  P. Funk,et al.  ECG sensor signal analysis to represent cases in a case-based stress diagnosis system , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[29]  Ahmet Akbas Evaluation of the physiological data indicating the dynamic stress level of drivers , 2011 .

[30]  Venkatesh Balasubramanian,et al.  EMG-based analysis of change in muscle activity during simulated driving , 2007 .