Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.
Muscle fatigue has widespread application in the field of rehabilitation medicine. The paper studies the muscle fatigue using surface electromyogram (sEMG) in the background of rehabilitation training system. The sEMG and ventilatory threshold of vastus lateralis, rectus femoris and erector spinae are collected synchronously and the electromyogram fatigue threshold (EMGFT) of different sEMG was analyzed by increasing load cycling experiments of 10 healthy subjects. This paper also analyzes the effect of isotonic and isometric contraction on EMGFT. Results showed that the appeared time of EMGFT was earlier than that of ventilatory threshold in the incremental load cycling. While the differences were subtle and EMGFT was verified to be effective. EMGFT has been proven effective for different muscle contraction by comparing the EMGFT of vastus lateralis and erector spinae. EMGFT could be used to keep muscle injuries from overtraining in the process of rehabilitation. Therefore, EMGFT has a great significance for femoral shaft fractures’s fatigue monitoring in rehabilitation training.
This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.
Mental fatigue is a subjective fatigue state caused by long-term brain activity, which is the core of health problems among brainworkers. However, its influence on the process of brain information transmission integration is not clear. In this paper, phase amplitude coupling (PAC) between theta and gamma rhythm was used to study the electroencephalogram (EEG) data recorded before and after mental fatigue, so as to explain the effect of mental fatigue on brain information transmission mechanism. The experiment used a 4-hour professional English reading to induce brain fatigue. EEG signals of 14 male undergraduate volunteers before and after mental fatigue were recorded by Neuroscan EEG system. Phase amplitude coupling value was calculated and analyzed. t test was used to compare the results between two states. The results showed that theta phase of more than 90% of the electrodes in the whole brain area jointly modulated gamma amplitude of the right central area and the right parietal area, and the coupling effect among different brain regions significantly decreased (P < 0.05) when participants had felt mental fatigue. This paper shows that phase amplitude coupling can explain the influence of mental fatigue on information transmission mechanism. It could be an important indicator for mental fatigue detection. On the other hand, the results also provide a new measure to evaluate the effect of neuromodulation in relieving mental fatigue.
Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.
ObjectiveTo assess the fatigue in patients with obstructive sleep apnea hypopnea syndrome (OSAHS), and analyze the factors caused fatigue and the relationship between quality of life (QOL) and fatigue. MethodsOne hundred and sixty-nine patients with OSAHS and 78 subjects without OSAHS diagnosed by polysomnography (PSG) between December 2010 and March 2011 in West China Hospital were recruited in the study. Fatigue was assessed by using multidimensional fatigue inventory (MFI), excessive daytime sleepiness by Epworth sleepiness scale(ESS), QOL by functional outcomes of sleep questionnaire (FOSQ). ResultsFatigue in the patients with OSAHS was more severe than that of the controls (51.06±13.39 vs. 44.82±9.81, P < 0.001), but no difference was revealed in the patients with different degree of OSAHS. Fatigue was positively correlated with ESS score(r=0.210), total sleep time intervals(r=0.156), and the ratio of time of SpO2 below 90% in total sleep time(r=0.153)(P < 0.05), and was negatively correlated with the average oxygen saturation(r=-0.171, P < 0.05) and all subscales of FOSQ(P < 0.01). ConclusionsFatigue in patients with OSAHS is more severe than that of controls. Fatigue can significantly reduce QOL, and the impact is greater than that of excessive daytime sleepiness.
Objective To explore the current situation and influencing factors of self-regulatory fatigue in maintenance hemodialysis (MHD) patients, so as to provide good dialysis treatment for MHD patients, reduce their level of self-regulated fatigue and improve their quality of life. Methods The convenient sampling method was used to select the MHD patients in the Wenjiang Hemodialysis Center of West China Hospital of Sichuan University between April 12 and April 30, 2022. The patients were investigated by self-made basic information scale and self-regulatory fatigue scale. Results A total of 131 patients were included. The average score of self-regulatory fatigue was 53.47±6.45, cognitive dimension was 20.21±2.39, emotional dimension was 20.85±2.85, behavioral dimension was 12.40±3.63. The results of multiple linear stepwise regression analysis showed that age, duration of dialysis and educational background could inversely predict the score of self-regulatory fatigue (P<0.05). Conclusions MHD patients have a high level of self-regulatory fatigue. Clinical nurses can make individual dialysis programs according to the actual situation of MHD patients, improve their self-regulated level and physical and mental health, and improve the quality of life of MHD patients.
The electroencephalographic characteristics of mental fatigue, which was induced by long-term working memory task of 2-back, were studied by event-related potential (ERP) technology in order to obtain objective evaluation indicators for mental fatigue. Thirty-two healthy male subjects, 22–28 years old, were divided into two groups evenly, one is un-fatigue group and the other is fatigue group. The fatigue group performed a 2-back task for 100 min continuously, while the un-fatigue group just performed a 2-back task at the first and last 10 min respectively, and rested during the middle 80 min. The subjective levels of fatigue, task performance and electroencephalogram were recorded. The impaired thought and attention states, enhanced sleepy and fatigue feeling were found in the fatigue group, meanwhile their reaction time to 2-back task extended, and the accuracy decreased significantly. These results verified the validity of mental fatigue model induced by 2-back task, and then the ERP characteristic parameters were compared and analyzed between fatigue group and un-fatigue group. The results showed that the fatigue group’s amplitudes of P300 (F = 2.539, P < 0.05) and error-related negativity (ERN) ( F = 10.040, P < 0.05) decreased significantly along with the increase of fatigue comparing with the un-fatigue group, however, there were no significant change in other parameters (all P > 0.05). These results demonstrate that P300 and ERN can be considered as potential evaluation indictors for mental fatigue induced by long-term working memory task, which will provide basis for the future exploring of countermeasure for mental fatigue.
To evaluate the fatigue behavior of nitinol stents, we used the finite element method to simulate the manufacture processes of nitinol stents, including expanding, annealing, crimping, and releasing procedure in applications of the clinical treatments. Meanwhile, we also studied the effect of the crown area dimension of stent on strain distribution. We then applied a fatigue diagram to investigate the fatigue characteristics of nitinol stents. The results showed that the maximum strain of all three stent structures, which had different crown area dimensions under vessel loads, located at the transition area between the crown and the strut, but comparable deformation appeared at the inner side of the crown area center. The cause of these results was that the difference of the area moment of inertia determined by the crown dimension induced the difference of strain distribution in stent structure. Moreover, it can be drawn from the fatigue diagrams that the fatigue performance got the best result when the crown area dimension equaled to the intermediate value. The above results proved that the fatigue property of nitinol stent had a close relationship with the dimension of stent crown area, but there was no positive correlation.