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Serving is the most important hitting technique in tennis, and a good service receiving can instantly reverse the active and passive relationship between serve and receive on the tennis court, and control the rhythm of the court. The purpose of this study is to use an adaptive neuron fuzzy intelligent system to analyze some techniques of tennis serve. In this study, eight male players from the school tennis team were selected as the experimental subjects, whose sports level was above the national tennis level II. Ten weeks before the simulation test, the training time and frequency of 8 subjects were the same. In other words, 5 times a week, 2.5 hours±0.5 hours. The work engineering of adaptive fuzzy system firstly, in the off-line modeling stage, the adaptive fuzzy system uses the rule self splitting technology to generate the initial fuzzy rules, and uses the improved adaptive neural network algorithm to optimize the calculation; then according to the error between the system input and the predicted output, the independent variable is adjusted and replaced; at the same time, the adaptive fuzzy system is further used for calculation In the process of tennis serving, the nonlinear control variables are obtained online and applied to the fuzzy system for control. Next, in the experiment, the system was used to record the body’s movement and service scores during service. The experimental results show that during the service process, the maximum trunk torsion amplitude can reach 48.26 ° and the minimum is only 5.41 ° and the service score accounts for 81.41% and 80.47% of the total scores of the two sections respectively. This shows that the fuzzy system in this study can effectively analyze the service posture and score of athletes. It is concluded that the accurate calculation and analysis of tennis serve by adaptive neuron intelligent fuzzy system in this study is conducive to improve the tennis serviceability and competition performance of players. This research has made a certain contribution to the intellectualization of sports. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) is a metabolic disease that seriously endangers the health of mothers and children. It is important to monitor GDM in real-time before diagnosis and to prevent it effectively.Objective: GDM was divided into the second trimester diagnosed diabetes mellitus (GDM_24) and the third trimester diagnosed diabetes mellitus (GDM_30). The risk prediction of two types of GDM was performed in normal pregnant women at 11-13 and 16-19 weeks of pregnancy, respectively.Methods: By stages, the K-W test was used to analyze the differences between basic information and energy metabolism factors, and multiple logistic regression was used to analyze the risk of energy metabolism factors and to correct the confounders with significant differences.Results: For the GDM_24 group, each additional unit of oxygen consumption (VO2), carbon dioxide production, and resting energy expenditure (REE) increased the risk by 2.4%, 3.5%, 0.4%, and 2.1%, 2.6%, and 0.3%, respectively, at 11-13 and 16-19 weeks of pregnancy. For the GDM_30 group, each additional unit of VO2 and REE was associated with an increased risk of 2.3% and 0.3%, respectively, at 16-19 weeks of pregnancy.Conclusion: The risk of GDM_30 only appeared in pregnant women during 16-19 weeks of pregnancy, which may indicate that GDM_24 and GDM_30 have different pathogenesis. [ABSTRACT FROM AUTHOR]

Fetal weight is one of the important indicators for judging whether fetal growth and development are normal. Fetal weight exceeding the normal range may lead to poor delivery outcomes. We aimed to establish a personalized fetal growth curve in order to effectively monitor fetal growth during pregnancy. Fetal weight can be monitored while fetal growth and development are assessed. This study retrospectively analyzed the birth weight and ultrasound database of 3,093 newborns delivered at normal term. The personalized fetal growth curve model was generated based on the birth weight formula established by Gardosi combined with the proportional weight equation. (1) The average birth weight of the single fetus at normal term was 3,457g. (2) According to the regression results of the proportion of fetal weight in full-term pregnancy and gestational week, the proportional weight equation is Weight% = 500.9 - 51.60GA + 1.727GA2- 0.01718GA3 (GA is gestational week), R2 is 98%, P< 0.001. In this study, the normal birth weight of newborns and normal range of fetal weight can be estimated by using the personalized fetal growth curve model. [ABSTRACT FROM AUTHOR]

Oral glucose tolerance test (OGTT) is the standard for the diagnosis of gestational diabetes mellitus (GDM). However, clinically, some cases with normal results were diagnosed as GDM in the third trimester. To establish a risk model based on energy metabolism, epidemiology, and biochemistry that could predict the GDM pregnant women with normal OGTT results in the second trimester. Qualitative and quantitative data were analyzed to find out the risk factors, and the binary logistic backward LR regression was used to establish the prediction model of each factor and comprehensive factor, respectively. The risk factors including the rest energy expenditure per kilogram of body weight, oxygen consumption per kilogram of body weight, whether to meet the weight gain criteria of the Institute of Medicine, the increase of body mass index between the second trimester and pre-pregnancy, and fasting blood glucose. By comparison, the comprehensive model had the best prediction performance, indicating that 85% of high-risk individuals were correctly classified. Energy metabolism, epidemiology, and biochemistry had better recognition ability for the GDM pregnant women with normal OGTT results in the second trimester. The addition of metabolic factors in the second trimester also improved the overall prediction performance. [ABSTRACT FROM AUTHOR]

Monitoring fetal weight during pregnancy has a guiding role in prenatal care. To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy. (1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns. (1) Delivery GA, weight at first visit, maternal height, pre-pregnancy body mass index, fetal sex, parity had significant effects on birth weight. Based on these parameters, the formula for calculating term optimal weight was obtained (R2= 22.8%, P< 0.001). (2) The personalized fetal growth curve was obtained according to the epidemiological factors input model of each pregnant woman. A model of personalized fetal growth curve can be established, and be used to evaluate fetal growth and development through estimated fetal weight monitoring. [ABSTRACT FROM AUTHOR]

The prediction of hypertensive disorders in pregnancy (HDP) mainly involves various aspects such as maternal characteristics and biomarkers. We aimed to study the effect of the HDP prediction model with or without placental growth factor (PlGF). This study used maternal factors and PlGF, and standardized the data uniformly. At 12–20 weeks, the comprehensive comparison of model quality with or without PlGF was conducted by logistic regression. The area under curve and the model accuracy of the model with PlGF were higher than those of the model without PlGF. The accuracy of the model with PlGF was above 90%. Adding PlGF to the model for predicting HDP improved the accuracy and effectiveness of the model. This study confirmed the predictive performance of PlGF. [ABSTRACT FROM AUTHOR]

Placental growth factor (PlGF), one of the biomarkers, has a certain predictive effect on hypertensive disorders in pregnancy (HDP).To study the HDP prediction effect of different methods for variable selection and modeling for models containing PlGF. For the model containing PlGF, the appropriate range of PlGF parameters needed to be selected. Step-logistic regression and lasso were used to compare the model effect of twice range selection. The PlGF model with good predictive effect and appropriate detecting gestational age was selected for the final prediction.The effect of the model containing PlGF tested at 15–16 weeks was better than the PlGF value without comprehensive screening. The sensitivity of both methods was over 92%. By comprehensive comparison, the final model of lasso method in this study was more effective.In this study, a variety of methods were used to screen models containing PlGF parameters. According to clinical needs and model effects, the optimal HDP prediction model with PlGF parameters in the second trimester of 15–26 weeks of pregnancy was finally selected. [ABSTRACT FROM AUTHOR]

Fetal ECG can be obtained in a non-invasive manner to monitor fetal growth status. In this study, a fetal heart rate calculation system was proposed, which consists of the fetal ECG recorder (MF-HOLTER) and the fetal ECG monitoring software (FECG-MS). The abdomen electrocardiogram (AECG) of pregnant woman is acquired through the MF-HOLTER. The FECG-MS packs the AECG data and calls the fetal ECG separation algorithm to obtain the separated FECG and the fetal QRS (FQRS) position. The fetal heart rate (FHR) is further obtained by calculating the R-R interval value. At the same time, this study proposed a FQRS position correction algorithm to calculate the correct FHR value. In order to verify the accuracy of the FHR calculation, the ECG signal of FLUKE’s PS320 FETAL SIMULATOR and clinical data were simultaneously tested. The accuracy rate is over 98% in processing the simulator’s data. In processing clinical data, the FHR values obtained by both the system proposed in this study and Monica AN24 are very close, and the difference is less than 1 bpm. The results show that the fetal heart rate calculation system is accurate and stable, and has a positive application value and prospect. [ABSTRACT FROM AUTHOR]

The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors. To establish models for early prediction and intervention of HDP. This study used the three types of risk factors and Support Vector Machine (SVM) to establish prediction models of HDP at different gestational weeks. The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28–34 weeks and ⩾ 35 weeks, it reached more than 92%. Multi-risk factors combined with dynamic gestational weeks’ prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) is not easily detected. The difficulty in detecting GDM is largely due to the late onset of clinical symptoms as well as the various complications that result from GDM [1].Objective: GDM greatly influences both mother and child. Therefore, the purpose of this study was to reduce the morbidity of GDM.Methods: In this study, risk factors that influence GDM were selected through statistical analysis. Multivariable logistic regression analysis was used to obtain the regression equation and Odds Ratio (OR) value. The risk score of each factor was obtained according to the OR value.Results: The score of every pregnant woman could be very intuitively used to show the risk of getting GDM.Conclusion: Through the above methods, a comprehensive risk evaluation method of detecting GDM was developed. [ABSTRACT FROM AUTHOR]

Background: Fetal electrocardiography (FECG) places electrodes on the maternal abdomen to convert the fetal electrocardiosignals into fetal heart rate (FHR), improving the accuracy and comfort of pregnant woman. At the same time, FECG simplifies the procedure of long term monitoring in the perinatal period.Objective: Investigating the influence of gestational age and time of day on FHR features to distinguish between non-stress test (NST) normal fetuses and NST suspicious fetuses.Methods: A novel method of FHR baseline estimation was presented; then baseline value and fetal heart rate variation (FHRV) were analyzed in the time domain using FHR signals recorded from 52 fetuses.Results: Baseline values in 1:00, 2:00, 4:00, 5:00 and heart rate variation (HRV) distribution showed a significant difference (p< 0.05) between NST normal fetuses and NST suspicious fetuses.Conclusions: The results suggest that NST normal and suspicious fetuses had same outcome and different FHR features. Accurately distinguishing normal fetuses and suspicious fetuses is important for lowering the false positive rate and reducing unnecessary clinical intervention. [ABSTRACT FROM AUTHOR]

Background: Postpartum hemorrhage (PPH) is the most common cause of parturient death worldwide [1]. However, most cases of PPH can be avoided.Objective: This paper employs statistical methods to screen risk factors of PPH and calculate relevant parameters.Methods: Multivariable logistic regression analysis was applied to obtain the regression equation and odds ratio (OR) value. The determined risk factors were assigned comprehensive and reasonable scores according to scientific relevance and reasoning according to the OR value. PPPH values were calculated in order to assess the morbidity of PPH.Results: The scores of pregnant women could be intuitively used to show the risk of getting PPH.Conclusions: Through the above methods, a comprehensive risk evaluation method of detecting PPH was developed. [ABSTRACT FROM AUTHOR]