A Survey on Fatigue Detection of Workers Using Machine Learning

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.

[1]  Nguyen Thanh Phuong,et al.  Virus diseases risk-factors associated with shrimp farming practices in rice-shrimp and intensive culture systems in Mekong Delta Viet Nam , 2015 .

[2]  Divya Chandan.  Drowsiness Detection System Using MATLAB , 2018 .

[3]  Chandrasekhar M. Patil,et al.  Development of a new methodology for iris algorithm in biometric authentication using hamming distance concepts , 2017, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).

[4]  Huiquan Zhang,et al.  Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns , 2018, IEEE Access.

[5]  Daniel A. Ashlock,et al.  Binary decision automata modelling stress in the workplace , 2013, 2013 IEEE Congress on Evolutionary Computation.

[6]  Zhitao Xiao,et al.  Driver Fatigue Detection Based on Eye State Recognition , 2017, 2017 International Conference on Machine Vision and Information Technology (CMVIT).

[7]  C. Jaya Bharathi.  Detection of Drowsiness in Human Eye usingSVM , 2014 .

[8]  Jiande Sun,et al.  Eye Recognition With Mixed Convolutional and Residual Network (MiCoRe-Net) , 2018, IEEE Access.

[9]  Manminder Singh,et al.  Face detection and eyes extraction using sobel edge detection and morphological operations , 2016, 2016 Conference on Advances in Signal Processing (CASP).

[10]  Tan Wencheng,et al.  Fatigue Reliability Analysis and Life Bench Test of Buffer Block in Car Damper , 2018, 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[11]  Abdellah Madani,et al.  Facial Expression Recognition Using Decision Trees , 2016, 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV).

[12]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[13]  Qiang Ji,et al.  A probabilistic framework for modeling and real-time monitoring human fatigue , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Yong Zhao,et al.  A practical driver fatigue detection algorithm based on eye state , 2010, 2010 Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia).

[15]  Manolya Kavakli,et al.  Sensor Applications and Physiological Features in Drivers’ Drowsiness Detection: A Review , 2018, IEEE Sensors Journal.

[16]  Trupti Dange,et al.  Eye Estimation to detect Drowsiness , 2013 .

[18]  A. G. Ramakrishnan,et al.  Eye detection using color cues and projection functions , 2002, Proceedings. International Conference on Image Processing.

[19]  Hanaa Mohsin,et al.  Pupil detection algorithm based on feature extraction for eye gaze , 2017, 2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA).

[20]  B. N. Manu,et al.  Facial features monitoring for real time drowsiness detection , 2016, 2016 12th International Conference on Innovations in Information Technology (IIT).

[21]  Haiyuan Wu,et al.  Face and facial feature extraction from color image , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[22]  Jingyu Yang,et al.  Driver fatigue detection technology in active safety systems , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[23]  Yao Lu,et al.  Human eye feature extraction based on segmented binarization , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).