A comprehensive review of approaches to detect fatigue using machine learning techniques

Facial expression is one form of communication which being non-verbal in nature precedes verbal communication in both origin and conception. Most of the existing methods for Automatic Facial Expression Recognition (AFER) are mainly focused on global feature extraction assuming that all facial regions contribute equal amount of discriminative information to predict the expression class. The detection and localization of facial regions that have significant contribution to expression recognition and extraction of highly discriminative feature distribution from those regions are not fully explored. The key contributions of the proposed work are developing novel feature distribution upon combining the discriminative power of shape and texture feature; determining the contribution of facial regions and identifying the prominent facial regions that hold abstract and highly discriminative information for expression recognition. The shape and texture features taken into consideration are Local Phase Quantization (LPQ), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). Multiclass Support Vector Machine (MSVM) is used while one versus one classification. The proposed work is implemented on CK+, KDEF, and JAFFE benchmark facial expression datasets. The recognition rate of the proposed work is 94.2% on CK+ and 93.7% on KDEF, which is significantly more than the existing handcrafted feature-based methods. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12700

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Fatigue is one of the most disabling symptoms in several neurological disorders and has an important cognitive component. However, the relationship between self-reported cognitive fatigue and objective cognitive assessment results remains elusive. Patients with post-COVID syndrome often report fatigue and cognitive issues several months after the acute infection. We aimed to develop predictive models of fatigue using neuropsychological assessments to evaluate the relationship between cognitive fatigue and objective neuropsychological assessment results. We conducted a cross-sectional study of 113 patients with post-COVID syndrome, assessing them with the Modified Fatigue Impact Scale (MFIS) and a comprehensive neuropsychological battery including standardized and computerized cognitive tests. Several machine learning algorithms were developed to predict MFIS scores (total score and cognitive fatigue score) based on neuropsychological test scores. MFIS showed moderate correlations only with the Stroop Color–Word Interference Test. Classification models obtained modest F1-scores for classification between fatigue and non-fatigued or between 3 or 4 degrees of fatigue severity. Regression models to estimate the MFIS score did not achieve adequate R2 metrics. Our study did not find reliable neuropsychological predictors of cognitive fatigue in the post-COVID syndrome. This has important implications for the interpretation of fatigue and cognitive assessment. Specifically, MFIS cognitive domain could not properly capture actual cognitive fatigue. In addition, our findings suggest different pathophysiological mechanisms of fatigue and cognitive dysfunction in post-COVID syndrome.

/pdf/neuropsychological-predictors-of-fatigue-in-post-covid-98o9vdn5.pdf

Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

Air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. Speech-based fatigue-state detection is proposed in this paper. The speech signal is preprocessed to further extract the Mel frequency cepstrum coefficient (MFCC) from speech discourse. The machine learning method is used in fatigue detection. However, single machine learning fatigue detection methods often have low detection accuracy. To solve this problem, an ensemble learning method based on self-adaption quantum genetic algorithm (SQGA) heterogeneous learning methods is proposed. Pattern-level and feature-level resampling are used to increase the differences in the base learner’s training dataset. To enlarge the diversity of single learners, k-nearest neighbor (KNN), Bayesian network (BN), back propagation neural network (BPNN) and support vector machine (SVM) are adopted for the heterogeneous ensemble. On this basis, finally, the detection result is obtained by weighted summation. The weight of each base learner was determined by SQGA. The SQGA method combines the quantum genetic algorithm with the adaptive strategy. The adaptive strategy includes adaptive adjustment of the quantum rotation gate, adaptive generation of crossover probability and adaptive generation of mutation probability. The experiments on real civil aviation radio land–air communication show that the proposed method can obtain 98.5% detection accuracy, with a 1.2% false and 3.0% missing report rate, whereas the SVM only obtains 94.0% detection accuracy, with a 5.4% false and 9.0% missing report rate.

https://www.mdpi.com/2076-3417/12/20/10252/pdf?version=1665568901

Fatigue Detection of Air Traffic Controllers Based on Radiotelephony Communications and Self-Adaption Quantum Genetic Algorithm Optimization Ensemble Learning

Real-time fatigue driving detection system based on multi-module fusion

Total wear in transient regime consists of transient wear and linear wear components. Within the transient regime metal possess higher wear rates than the steady state. FEM computational based wear models ignore transient regime which leads to higher error. This paper emphases on predicting the total wear in transient regime of 316 stainless steel pin against AISI 52100 bearing steel disc for 20 N and 30 N.Novel methodology is proposed to calculate individual wear components. Linear component by FEM based Abaqus software using an extrapolation technique to reduce computational time without compromising the accuracy. Transient component is calculated using Gauss Newton regression method. Further, total wear is modelled by addition of linear and transient wear components. Simulated total wear has good agreement with experimental wear error within 11% for both the loads. Corresponding transient wear rate was found to be approximately 2.5 times higher than steady state wear. This establishes the robustness of proposed methodology. 

Transient Wear FEA Modelling Using Extrapolation Technique for Steel-on-Steel Dry Sliding Contact

A-Priori Screening of Deep Eutectic Solvent for Enhanced Oil Recovery Application Using COSMO-RS Framework

Thermodynamic modelling of physico-chemical properties of new generation ionic liquid-bitumen system

Bladeless fans are more energy efficient, safer due to the hidden blades, easier to clean, and more adjustable than conventional fans. This paper investigates the influence of the airfoil’s outlet slit thickness on the discharge ratio by varying the outlet slit thickness of an Eppler 473 airfoil from 1.2 mm to 2 mm in intervals of 0.2 mm by using a k-omega SST turbulence model with an all y+ wall treatment used to numerically simulate in CFD. The computational results indicated that smaller slits showed higher discharge ratios. The airfoil with a 1.2 mm slit thickness showed a discharge ratio of 18.78, a 24% increase from the discharge ratio of the 2 mm slit. The effect of outlet angle on the pressure drop across the airfoil was also studied. Outlet angles were varied from 16° to 26° by an interval of 2°. The airfoil profile with a 24° outlet angle showed a maximum pressure difference of 965 Pa between the slit and leading edge. In contrast, the 16° outlet angle showed the least pressure difference of 355 Pa. Parameters such as average velocity (U), turbulent kinetic energy, the standard deviation of velocity, and outlet velocity magnitude are used to assess the performance of airfoil profiles used in bladeless fan.

/pdf/determination-of-optimum-outlet-slit-thickness-and-outlet-1j1gdhqr.pdf

Vedant Joshi

Papers

A comprehensive review of approaches to detect fatigue using machine learning techniques

Transient Wear FEA Modelling Using Extrapolation Technique for Steel-on-Steel Dry Sliding Contact

A-Priori Screening of Deep Eutectic Solvent for Enhanced Oil Recovery Application Using COSMO-RS Framework

Thermodynamic modelling of physico-chemical properties of new generation ionic liquid-bitumen system

Determination of Optimum Outlet Slit Thickness and Outlet Angle for the Bladeless Fan Using the CFD Approach