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.
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.
【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.
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.
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.
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.
This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.
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.
In order to evaluate the safety performance of self-expandable NiTi alloy stents systematically, the dynamic safety factor drawn up by International Organization for Standardization, was used to quantitatively reflect the safety performance of stents. Based on the constitutive model of super-elastic memory alloy material in Abaqus and uniaxial tensile test data of NiTi alloy tube, finite element method and experiments on accelerated fatigue life were carried out to simulate the self-expansion process and the shape change process under the action of high and low blood pressure for three L-type stents of Φ8×30 mm, Φ10×30 mm, Φ12×30 mm. By analyzing the changes of stress and strain of self-expanding NiTi alloy stent, the maximum stress and strain, stress concentration position, fatigue strength and possible failure modes were studied, thus the dynamic safety factor of stent was calculated. The results showed that the maximum stress and plastic strain of the stent increased with the increase of grip pressure, but the maximum stress and strain distribution area of the stent had no significant change, which were all concentrated in the inner arc between the support and the connector. The dynamic safety factors of the three stents were 1.31, 1.23 and 1.14, respectively, which indicates that the three stents have better safety and reliability, and can meet the fatigue life requirements of more than 10 years, and safety performance of the three stents decreases with the increase of stent’s original diameter.
