At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.
The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.
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.
Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.
In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.
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.
Heart rate variability (HRV) is an important point to judge a person’s state in modern medicine. This paper is aimed to research a person’s fatigue level connected with vagal nerve based on the HRV using the improved Welch method. The process of this method is that it firstly uses a time window function on the signal to be processed, then sets the length of time according to the requirement, and finally makes frequency domain analysis. Compared with classical periodogram method, the variance and consistency of the present method have been improved. We can set time span freely using this method (at present, the time of international standard to measure HRV is 5 minutes). This paper analyses the HRV’s characteristics of fatigue crowd based on the database provided by PhysioNet. We therefore draw the conclusion that the accuracy of Welch analyzing HRV combining with appropriate window function has been improved enormously, and when the person changes to fatigue, the vagal activity is diminished and sympathetic activity is raised.
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.
【Abstract】Objective To establish and assess the rat model of postoperative fatigue syndrome (POFS). Methods The rat model of POFS was developed by the partial resection of the liver. The behavioral changes prior and post to operation, the disorder of nutritive intake after operation, stress reaction (pathological changes of mucous membrane in small intestine) and the hepatic albumin gene expression were observed. Results Low body temperature, lower sensitivity and reactivity were found. The serum levels of the iron, total protein, albumin, globulin and so on as the indexes of nutrition obviously dropped. The injury of the mucous membrane resulted from the stress reaction after the resection of the liver. The gene expression of the albumin decreased in the model group.Conclusion The experimental rat model of POFS by partial resection of the liver can be used for the investigation of POFS.
ObjectiveTo systematically review the clinical features of chronic fatigue syndrome (CFS) cases with pathogens infection.
MethodsWe electronically searched databases including VIP, WanFang Data, CNKI, CBM, PubMed, MEDLINE, EMbase, The Cochrane Library, Web of Science, Elsevier and Google Scholar from 1994 to 2014 for CFS-related studies. Two reviewers independently screened literature and extracted data. Then we systematically reviewed and analyzed the information on demographic characteristics, clinical manifestations, types of infected pathogens, and results of some biochemical examinations.
ResultsA total of 84 studies (case reports and case series) involving 2 565 CFS cases from 18 countries were included. The major infected pathogens of included CFS cases were mycoplasma, EB virus, intestinal virus, Bernat rickettsia, human-herpes virus, and Gram-negative intestinal bacteria. Fifty-seven studies reported that there might be associations between the pathogenic infection and CFS pathogenesis. Although there were different types of CFS-related pathogens, almost all the studies inferred that pathogens infection linked with immune dysfunction, which might cause CFS symptoms.
ConclusionThere may be associations between the pathogenic infection and CFS pathogenesis.
