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.
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.
【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.
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.
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4–3 Hz), θ wave (4–7 Hz), α wave (8–13 Hz) and β wave (14–30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
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.
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.
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.
ObjectiveTo investigate the methodological and reporting quality of clinical trials involving Xiaoyao San for chronic fatigue syndrome. MethodsWe searched PubMed, CBM, CNKI, VIP and WanFang Data to identify randomized controlled trials (RCTs) about Xiaoyao San for chronic fatigue syndrome. The methodological and reporting quality of included RCTs was respectively evaluated according to the assessment tool of risk of bias of the Cochrane Handbook 5.1.0 and the CONSORT 2010 statement, combined with complementary assessment by the characteristic indicators of traditional Chinese medicine (TCM). The methodological and reporting quality of included case series study was respectively assessed by the methods recommended by the Britain's National Institute for Clinical Excellence (NICE) and the STROBE statement. ResultsA total of 27 clinical trials were included, involving 11 RCTs and 16 case series studies. According to the assessment tool of risk of bias of the Cochrane Handbook, 54.5% of the RCTs performed proper random method, 9.1% conducted allocation concealment and blinding, 72.7% selected intention-to-treat (ITT) analysis without the report of loss to follow-up, and no RCT existed selective reports. Corresponding to the characteristic indicators of TCM, 54.5% of the RCTs did not conduct TCM syndrome diagnosis, the curative effect standard of TCM syndrome was discrepant, and no RCT was multi-center study. The CONSORT 2010 statement indicated that no RCT explained sample size estimation, implementation details of randomization, flow diagram of participant, use of ITT and clinical trial registration. According to the items recommended by Britain's NICE, 6.25% of the case series studies were multi-center, 81.25% did not report clear inclusion and exclusion criteria, and no case series study performed continuous patient recruitment and stratification analysis of outcome. The STROBE statement indicated that no case series study reported research design, sample size, flow chart, bias, limitations and generalizability. ConclusionThe quality of clinical trials about Xiaoyao San for chronic fatigue syndrome is still low in methodological and reporting aspects. It is suggested that the future clinical trials should be conducted with references of CONSORT statement and STROBE statement, to propel the modernization and internationalization of TCM.
With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.