Mental Fatigue State Research Articles

Mental fatigue impairs physical performance in humans

Mental fatigue is a psychobiological state caused by prolonged periods of demanding cognitive activity. Although the impact of mental fatigue on cognitive and skilled performance is well known, its effect on physical performance has not been thoroughly investigated. In this randomized crossover study, 16 subjects cycled to exhaustion at 80% of their peak power output after 90 min of a demanding cognitive task (mental fatigue) or 90 min of watching emotionally neutral documentaries (control). After experimental treatment, a mood questionnaire revealed a state of mental fatigue (P = 0.005) that significantly reduced time to exhaustion (640 +/- 316 s) compared with the control condition (754 +/- 339 s) (P = 0.003). This negative effect was not mediated by cardiorespiratory and musculoenergetic factors as physiological responses to intense exercise remained largely unaffected. Self-reported success and intrinsic motivation related to the physical task were also unaffected by prior cognitive activity. However, mentally fatigued subjects rated perception of effort during exercise to be significantly higher compared with the control condition (P = 0.007). As ratings of perceived exertion increased similarly over time in both conditions (P < 0.001), mentally fatigued subjects reached their maximal level of perceived exertion and disengaged from the physical task earlier than in the control condition. In conclusion, our study provides experimental evidence that mental fatigue limits exercise tolerance in humans through higher perception of effort rather than cardiorespiratory and musculoenergetic mechanisms. Future research in this area should investigate the common neurocognitive resources shared by physical and mental activity.

Lempel-Ziv complexity changes and physiological mental fatigue level during different mental fatigue state with spontaneous EEG

The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.

Feature Extraction for Mental Fatigue and Relaxation States Based on Systematic Evaluation Considering Individual Difference

Feature extraction for mental fatigue and relaxation states is helpful to understand the mechanisms of mental fatigue and search effective relaxation technique in sustained work environments. Experiment data of human states are often affected by external and internal factors, which increase the difficulties to extract common features. The aim of this study is to explore appropriate methods to eliminate individual difference and enhance common features. Mental fatigue and relaxation experiments are executed on 12 subjects. An integrated and evaluation system is proposed, which consists of subjective evaluation (visual analogue scale), calculation performance and neurophysiological signals especially EEG signals. With consideration of individual difference, the common features of multi-estimators testify the effectiveness of relaxation in sustained mental work. Relaxation technique can be practically applied to prevent accumulation of mental fatigue and keep mental health. The proposed feature extraction methods are widely applicable to obtain common features and release the restriction for subjection selection and experiment design.

ESTIMATION OF MENTAL FATIGUE BASED ON WAVELET PACKET PARAMETERS AND KERNEL LEARNING ALGORITHMS

A new method by combining wavelet packet transform with kernel learning algorithms is proposed to estimate the mental fatigue state in this paper. The first step of this method is to investigate the impact of long term mental arithmetic task on psychology and physiology of subjects by subjective self-reporting measures, action performance test, power spectral indices of HRV and wavelet packet parameters of EEG. The second step is to calculate the wavelet packet features of all EEG data segments, including relative wavelet packet energy parameters in four frequency bands, wavelet packet entropy and three ratio indices. Finally, kernel principal component analysis (KPCA) and support vector machine (SVM) are jointly applied to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve higher classification accuracy (88%) of the mental fatigue state. Hence KPCA-SVM could be a promising model for the estimation of mental fatigue.

Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

Mental fatigue is an extremely sophisticated phenomenon, which is influenced by the environment, the state of health, vitality and the capability of recovery. A single parameter cannot fully describe it. In this paper, the effects of long time sustained low-workload visual display terminal (VDT) task on psychology are investigated by subjective self-reporting measures. Then power spectral indices of HRV, the P300 components based on visual oddball and wavelet packet parameters of EEG are combined to analyze the impacts of prolonged visual display terminal (VDT) activity on autonomic nervous system and central nervous system. Finally, wavelet packet parameters of EEG are extracted as the features of brain activity in different mental fatigue states. Kernel principal component analysis (KPCA) and support vector machine (SVM) are jointly applied to differentiate two states. The statistic results show that the level of both subjective sleepiness and fatigue increase significantly from pre-task to post-task, which indicate that the long time VDT task induces the mental fatigue to the subjects. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. The P300 components and wavelet packet parameters of EEG are strongly related with mental fatigue. Moreover, the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve a high recognition accuracy (87%) of mental fatigue state. Multipsychophysiological measures and KPCA-SVM method could be a promising tool for the evaluation of mental fatigue.

Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM

The impacts of prolonged visual display terminal (VDT) work on central nervous system and autonomic nervous system are observed and analyzed based on electroencephalogram (EEG) and heart rate variability (HRV). Power spectral indices of HRV, the P300 components based on visual oddball task, and multichannel linear descriptors of EEG are combined to estimate the change of mental fatigue. The results show that long-term VDT work induces the mental fatigue. The power spectral of HRV, the P300 components, and multichannel linear descriptors of EEG are correlated with mental fatigue level. The cognitive information processing would come down after long-term VDT work. Moreover, the multichannel linear descriptors of EEG can effectively reflect the changes of θ, α, and β waves and may be used as the indices of the mental fatigue level. The kernel principal component analysis (KPCA) and hidden Markov model (HMM) are combined to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-HMM method can effectively reduce the dimensions of the feature vectors, accelerate the classification speed, and improve the accuracy of mental fatigue to achieve themaximum 88%. Hence KPCA-HMM could be a promising model for the estimation of mental fatigue.

Hand-held monitor of sympathetic nervous system using salivary amylase activity and its validation by driver fatigue assessment

In order to realize a hand-held monitor of the sympathetic nervous system, we fabricated a completely automated analytical system for salivary amylase activity using a dry-chemistry system. This was made possible by the fabrication of a disposable test-strip equipped with built-in collecting and reagent papers and an automatic saliva transfer device. In order to cancel out the effects of variations in environmental temperature and pH of saliva, temperature- and pH-adjusted equations were experimentally determined, and each theoretical value was input into the memory of the hand-held monitor. Within a range of salivary amylase activity between 10 and 140 kU/l, the calibration curve for the hand-held monitor showed a coefficient with R(2)=0.97. Accordingly, it was demonstrated that the hand-held monitor enabled a user to automatically measure the salivary amylase activity with high accuracy with only 30 microl sample of saliva within a minute from collection to completion of the measurement. In order to make individual variations of salivary amylase activity negligible during driver fatigue assessment, a normalized equation was proposed. The normalized salivary amylase activity correlated with the mental and physical fatigue states. Thus, this study demonstrated that an excellent hand-held monitor with an algorithm for normalization of individuals' differences in salivary amylase activity, which could be easily and quickly used for evaluating the activity of the sympathetic nervous system at any time. Furthermore, it is suggested that the salivary amylase activity might be used as a better index for psychological research.

Evaluation of mental fatigue using feature parameter extracted from event-related potential

An attempt was made to evaluate mental fatigue induced during a VDT task using feature parameters extracted from an event-related potential (ERP) P300. Since the peak of the grand-averaged P300 waveform was not clear, it was sometimes difficult to detect the amplitude and the latency. The removal of the noisy electroencephalography (EEG) waveform based on the cross-correlation between a grand-averaged waveform and each waveform was found to be effective for making the P300 waveform clear. The parameter extraction methods using a principal component analysis and temporal changes of the cross-correlation function between the grand-averaged waveform and each waveform were used to evaluate mental fatigue. The P300b component and the standard deviation of the lime lag τ that corresponded to the maximum cross-correlation between the grand-averaged waveform and each EEG waveform were found to reflect some aspects of mental fatigue (the decreased function of cognitive information processing). Relevance to industry Fatigue, especially mental fatigue, is inevitable for office workers and in life in general. Fatigue is usually related to a loss of efficiency and disinclination to effort. It is important to manage and cope with fatigue so that the workers do not damage their health. It is also possible that cumulative mental fatigue leads to decreased productivity in the workplace and induces critical errors in the worst cases. Therefore, the management of mental fatigue is important from the viewpoint of occupational risk management, productivity, and occupational health. This study presents a systematic approach to the evaluation of mental fatigue induced during a VDT task. The proposed method provides a useful means for evaluating the state of mental fatigue, which would be potentially applicable to the management of fatigue from the three viewpoints listed above and would lead to increased agility in an organization.

MEMSWEAR – INCORPORATING MEMS TECHNOLOGY INTO SMART SHIRT FOR GERIATRIC HEALTHCARE

International Journal of Computational Engineering ScienceVol. 04, No. 02, pp. 289-292 (2003) BioMEMS and MicrofluidsNo AccessMEMSWEAR – INCORPORATING MEMS TECHNOLOGY INTO SMART SHIRT FOR GERIATRIC HEALTHCAREKWONG-LUCK TAN and FRANCIS E. H. TAYKWONG-LUCK TANMedical Devices Group, Institute of Bioengineering and Nanotechnology, 51 Science Park Road #01-01/10, The Aries, Singapore Science Park II, Singapore 117586, Singapore Search for more papers by this author and FRANCIS E. H. TAYMedical Devices Group, Institute of Bioengineering and Nanotechnology, 51 Science Park Road #01-01/10, The Aries, Singapore Science Park II, Singapore 117586, SingaporeDepartment of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore Search for more papers by this author https://doi.org/10.1142/S1465876303001101Cited by:1 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractCurrent vital signs monitoring system used by hospitals are bulky and usually for measurement of one or two vital signs specifically. These devices are inconvenient for home use and too technical to layman. With a comprehensive vital sings monitor in the form of a garment, MEMSWear, the hassle of hanging cables and bulky equipment can be eliminated. MEMSWear will benefit the elderly by improving the overall standard of the healthcare system and reducing the costs of in-house monitoring and hospitalization. This paper presents the vision and methodology of the MEMSWear project. In addition, the development of a novel human fall detection system using MEMS technology is discussed. Finally, some preliminary results of the vital signs monitoring are presented.Keywords:MEMSgeriatric healthcarewireless communicationvital signs monitoringfall detection system References Health Facts Singapore 2001 . Google ScholarS. Parket al., The Wearable Mother-boardTM: An Information Infrastructure or Sensate Liner for Medical Applications, Studies in Health Technology and Informatics 62 (IOS Press, 1999) pp. 252–258. Google Scholar FiguresReferencesRelatedDetailsCited By 1Detection of mental fatigue state with wearable ECG devicesShitong Huang, Jia Li, Pengzhu Zhang and Weiqiang Zhang1 Nov 2018 | International Journal of Medical Informatics, Vol. 119 Recommended Vol. 04, No. 02 Metrics History KeywordsMEMSgeriatric healthcarewireless communicationvital signs monitoringfall detection systemPDF download

Motivational Antecedents of Empathy: Inhibiting Effects of Fatigue

Two experiments investigated the joint effect of need for cognitive closure (induced by mental fatigue) and similarity of perspective on empathic responding. Results indicated that when participants felt similar to a distressed target, no differences in empathy emerged across fatigue conditions. When they felt dissimilar, participants in the high (vs. low) mental fatigue condition were less likely to accept the target's response as appropriate and reported less empathic concern for the target. Furthermore, when motivated to learn more about the target because of an outcome dependency manipulation (introduced in Experiment 2) the reduced empathy observed under fatigue was corrected. The results imply that fatigue-induced need for cognitive closure acts as a motivational boundary condition of empathic responding.