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To assess the early postpartum pelvic floor function in twin pregnancies of different conception modalities by measuring surface electromyography of the pelvic floor muscles with the Glazer protocol. This retrospective study analyzed 241 twin pregnancies delivered via cesarean section at the International Peace Maternity and Child Health Hospital (IPMCHH), affiliated with Shanghai Jiao Tong University School of Medicine, between March 2019 and December 2023. Participants underwent pelvic floor function assessments 42-60 days postpartum. Pelvic floor muscle activity was evaluated using surface electromyography (sEMG) following the Glazer protocol. Univariate and multivariable logistic regression analyses were performed to assess the impact of conception modes (natural vs ART) on early postpartum pelvic floor function in twin pregnancies. The mean anterior resting phase amplitude was 4.80 ± 5.23 μV in the ART group versus 6.38 ± 6.30 μV in the naturally conceived group. Similarly, the posterior resting phase amplitude measured 5.15 ± 5.28 μV (ART) and 6.78 ± 7.67 μV (natural conception). The total Glazer score differed significantly between groups, with ART pregnancies scoring 74.80 ± 14.82 and natural conception pregnancies scoring 67.57 ± 21.57 (P < 0.05). Univariate and multivariable logistic regression analyses confirmed that the total Glazer score during the early postpartum period was independently associated with conception mode (P < 0.05). Women with twin pregnancies conceived via ART may exhibit marginally improved pelvic floor function during the early postpartum period (6-8 weeks) compared to naturally conceived counterparts, potentially attributable to elevated estrogen levels associated with ART.

The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the mAP@0.5 could reach 82.3%, and mAP@0.5–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.

This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods.

The spiking neural network (SNN) is the third generation of artificial neural networks. The transmission and expression of information in SNN are performed by spike trains, making the SNN have the advantages of high calculation speed and low power consumption. Recently, researchers have employed the SNN to recognize surface electromyography (sEMG) signals, but problems are still left. The sEMG encoders may cause information loss, and the network decoders may cause poor training performance. The strength of the neuron stimulated can be expressed by the frequency of the input or output spikes (namely firing rate). Inspired by the firing rate principle, we proposed the smoothed frequency-domain decomposition encoder, which converts the sEMG to spike trains. Furthermore, we also proposed the network efferent energy decoder, which converts the network output to recognizing results. The employed SNN is a three-layer fully-connected network trained by the grey wolf optimizer. The proposed methods are verified by a hand gestures recognition task. A total of 11 subjects participated in the experiment, and sEMG signals were acquired from five commonly used hand gestures by three sEMG sensors. The results indicate that the loss function can be reduced to below 0.4, and the average gesture recognizing accuracy is 91.21%. These results show the potential of using the proposed methods for the actual prosthesis. In the future, we will optimize the SNN training method to improve the training speed and stability.

This study assessed muscle activity (root mean square, RMS, and median frequency, MDF) to evaluate the acute response to blood flow restriction (BFR) resistance exercise (RE) and conventional moderate intensity (MI) RE. We also performed exploratory analyses of differences based on sex and exercise‐induced hypoalgesia (EIH). Fourteen asymptomatic individuals performed four sets of unilateral leg press with their dominant leg to volitional fatigue under two exercise conditions: BFR RE and MI RE. Dominant side rectus femoris (RF) and vastus lateralis (VL) muscle activity were measured using surface electromyography (sEMG) through exercise. RMS and MDF were calculated and compared between conditions and timepoints using a linear mixed model. Pressure pain thresholds (PPT) were tested before and immediately after exercise and used to quantify EIH. Participants were then divided into EIH responders and nonresponders, and the differences on RMS and MDF were compared between the two groups using Hedges' g. RMS significantly increased over time (RF: p = 0.0039; VL: p = 0.001) but not between conditions (RF: p = 0.4; VL: p = 0.67). MDF decreased over time (RF: p = 0.042; VL: p < 0.001) but not between conditions (RF: p = 0.74; VL: p = 0.77). Consistently lower muscle activation was found in females compared with males (BRF, RF: g = 0.63; VL, g = 0.5. MI, RF: g = 0.72; VL: g = 1.56), with more heterogeneous findings in MDF changes. For BFR, EIH responders showed greater RMS changes (Δ RMS) (RF: g = 0.90; VL: g = 1.21) but similar MDF changes (Δ MDF) (RF: g = 0.45; VL: g = 0.28) compared to nonresponders. For MI, EIH responders demonstrated greater increase on Δ RMS (g = 0.61) and decrease on Δ MDF (g = 0.68) in RF but similar changes in VL (Δ RMS: g = 0.40; Δ MDF: g = 0.39). These results indicate that when exercising to fatigue, no statistically significant difference was observed between BFR RE and conventional MI RE in Δ RMS and Δ MDF. Lower muscle activity was noticed in females. While exercising to volitional fatigue, muscle activity may contribute to EIH.

Background: Forceps-assisted vaginal delivery is closely associated with postpartum pelvic floor muscle (PFM) injury and postpartum pelvic floor dysfunction. The present study utilized Glazer PFM surface electromyography (sEMG) and International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form (ICIQ-UI-SF) for the objective assessment of postpartum PFM function to determine the effects of different forceps delivery indications on early postpartum pelvic floor function in primiparas. Methods: Four hundred primiparas whose pregnancies had been terminated by forceps delivery were divided into three groups based on the indication for forceps delivery: fetal distress (FD) (n = 260), prolonged second stage of labor (PSSL) (n = 30), and intrapartum fever combined with fetal distress (IFFD) (n = 110). Pelvic floor muscle surface electromyography (EMG) performed according to the Glazer protocol at 42–60 days postpartum was the primary outcome measure. Results: The overall Glazer assessment scores of the PSSL (54.4±18.6) and IFFD (54.6±15.8) groups were significantly lower than that of the FD group (59.3± 17.0) (p = 0.019). The peak EMG value during the fast-twitch stage for the FD, PSSL, and IFFD groups was 32.4±17.7, 31.7±26.1, and 26.5±12.2μ V, respectively; the IFFD and FD groups were significantly different (p < 0.05). The incidence of postpartum stress urinary incontinence (SUI) was significantly higher in the IFFD and PSSL groups; the IFFD and FD groups were significantly different (p <0.05). Conclusions: Intrapartum fever probably affects the early postpartum pelvic floor function of primiparas who underwent forceps delivery, which mainly manifests in the short term as reduced fast-twitch muscle strength and SUI.

Epidermal sensors for remote healthcare and performance monitoring require the ability to operate under the effects of bodily motion, heat, and perspiration. Here, the use of purpose‐synthesized polymer‐based dry electrodes and graphene‐based strain gauges to obtain measurements of swallowed volume under typical conditions of exercise is evaluated. The electrodes, composed of the common conductive polymer poly(3,4 ethylenedioxythiophene) (PEDOT) electrostatically bound to poly(styrenesulfonate)‐b‐poly(poly(ethylene glycol) methyl ether acrylate) (PSS‐b‐PPEGMEA), collect surface electromyography (sEMG) signals on the submental muscle group, under the chin. Simultaneously, the deformation of the surface of the skin is measured using strain gauges comprising single‐layer graphene supporting subcontinuous coverage of gold and a highly plasticized composite containing PEDOT:PSS. Together, these materials permit high stretchability, high resolution, and resistance to sweat. A custom printed circuit board (PCB) allows this multicomponent system to acquire strain and sEMG data wirelessly. This sensor platform is tested on the swallowing activity of a cohort of 10 subjects while walking or cycling on a stationary bike. Using a machine learning (ML) model, it is possible to predict swallowed volume with absolute errors of 36% for walking and 43% for cycling. A wearable sensor platform for detecting swallow volume during exercise is studied. Participants wear a stretchable organic sensor set and perform exercises during which they swallow various volumes of water. The data are collected, transmitted to a cellphone,  and used to train a machine learning algorithm. The sensors show promising durability and sensitivity, while the algorithm has reasonable accuracy, especially at higher swallow volumes.

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

Inspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient-ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure [Formula: see text], derived from esophageal pressure ([Formula: see text]), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity. A prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the [Formula: see text]-time product ([Formula: see text]), the transdiaphragmatic pressure-time product ([Formula: see text]) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and [Formula: see text] was assessed by determining a neuromechanical conversion factor [Formula: see text] between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements. In total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with [Formula: see text] ([Formula: see text] and [Formula: see text] for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with [Formula: see text] ([Formula: see text]). The neuromechanical conversion factor obtained by fitting ETP to [Formula: see text] varied widely between patients ([Formula: see text]) and was highly correlated with the scalar determined during occlusions ([Formula: see text], [Formula: see text]). The occlusion-based method for deriving [Formula: see text] from ETP showed a breath-wise deviation to [Formula: see text] of [Formula: see text] across all datasets. These results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation.

Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.