An auditory vigilance task for mental fatigue detection

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

Using dual tasks to test immediate transfer of training between naturalistic movements: a proof-of-principle study

Theories of motor learning predict that training a movement reduces the amount of attention needed for its performance (i.e., more automatic). If training one movement transfers, then the amount of attention needed for performing a second movement should also be reduced, as measured under dual task conditions. The authors' purpose was to test whether dual task paradigms are feasible for detecting transfer of training between two naturalistic movements. Immediately following motor training, subjects improved performance of a second untrained movement under single and dual task conditions. Subjects with no training did not. Improved performance in the untrained movement was likely due to transfer, and suggests that dual tasks may be feasible for detecting transfer between naturalistic actions.

EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate

OBJECTIVE

Automatic measurement and monitoring of mental fatigue are invaluable for preventing mental-fatigue related accidents. We test an EEG-based mental-fatigue monitoring system using a probabilistic-based support vector-machines (SVM) method.

METHODS

Ten subjects underwent 25-h sleep deprivation experiments with EEG monitoring. EEG data were segmented into 3-s long epochs and manually classified into 5 mental-fatigue levels, based on subjects' performance on an auditory vigilance task (AVT). Probabilistic-based multi-class SVM and standard multi-class SVM were compared as classifiers for distinguishing mental fatigue into the 5 mental-fatigue levels.

RESULTS

Accuracy of the probabilistic-based multi-class SVM was 87.2%, compared to 85.4% using the standard multi-class SVM. Using confidence estimates aggregation, accuracy increased to 91.2%.

CONCLUSIONS

Probabilistic-based multi-class SVM not only gives superior classification accuracy but also provides a valuable estimate of confidence in the prediction of mental fatigue level in a given 3-s EEG epoch.

SIGNIFICANCE

The work demonstrates the feasibility of an automatic EEG method for assessing and monitoring of mental fatigue. Future applications of this include traffic safety and other domains where measurement or monitoring of mental fatigue is crucial.

A feature selection method for multilevel mental fatigue EEG classification

Two feature selection approaches for multilevel mental fatigue electroencephalogram (EEG) classification are presented in this paper, in which random forest (RF) is combined with the heuristic initial feature ranking scheme (INIT) or with the recursive feature elimination scheme (RFE). In a "leave-one-proband-out" evaluation strategy, both feature selection approaches are evaluated on the recorded mental fatigue EEG time series data from 12 subjects (each for a 25-h duration) after initial feature extractions. The latter of the two approaches performs better both in classification performance and more importantly in feature reduction. RF with RFE achieved its lowest test error rate of 12.3% using 24 top-ranked features, whereas RF with INIT reached its lowest test error rate of 15.1% using 64 top-ranked features, compared to a test error rate of 22.1% using all 304 features. The results also show that 17 key features (out of 24 top-ranked features) are consistent between the subjects using RF with RFE, which is superior to the set of 64 features as determined by RF with INIT.