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Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.

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Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.

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BACKGROUND: Research on muscle activity to reduce injuries during firefighting training has getting increasing attention. OBJECTIVE: The purpose of this study was to assess the activity changes in nine muscles of firefighters during the seven firefighting training programs, and to analyze the influence of different firefighting training programs on muscle activity. METHODS: Ten healthy male firefighters were recruited to measure the field surface electromyographic activities (including the percentage of Maximum Voluntary Contraction electromyography (% MVC) and the integrated electromyography value (iEMG)) during all the firefighting training programs. RESULTS: The results showed that the electromyographic activity of gastrocnemius (GA) was stronger in climbing the hooked ladder and climbing the six-meter long ladder training programs. Arms, shoulders, and lower limb muscles were more activated, myoelectric activities were more intense, and fatigue in these areas was more likely to occur during climbing five-story building with loads. Compared with other muscles, erector spine (ES) had a higher degree of activation during different postures of water shooting. The Borg scale scores of shoulders, trunk, thighs and calves were also higher. CONCLUSION: After completing all training programs, GA, tibialis anterior (TA), trapezius (TR), and ES were strongly activated, and all muscles had obvious force. The % MVC and iEMG analyses correspond well with the Borg Scale score. The results can provide certain reference for reducing the musculoskeletal injury of firefighters, carrying out scientific training and formulating effective injury prevention measures for them.

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OBJECTIVE: To evaluate the surface electromyography (sEMG) of pelvic floor muscles (PFMs), compare between vaginal birth and cesarean section and correlate with maternity and obstetrics characteristics in primiparous 6-8 weeks postpartum. METHODS: PFMs surface electromyography screening data of primiparous postpartum women in our hospital at 6-8 weeks postpartum from 2018 to 2021 were selected and analyzed. The study collected data on delivery activities of 543 postpartum women totally. RESULTS: In general, the abnormal incidence of pelvic floor electromyography in postpartum women mainly occurred in slow muscle (type I fiber) stage and endurance testing stage. Compared to vaginal birth postpartum women, the incidence of abnormal pelvic floor electromyography in cesarean section postpartum women is lower. There were statistical differences in measurement values of pelvic floor electromyography in several different stages between cesarean section and vaginal birth (P < 0.005). Regarding the influence on pelvic floor electromyography, there were more influencing factors on vaginal birth postpartum women including age, height, weight, weight gain during pregnancy, gestational week, and first and second stage of labor than on cesarean section postpartum women whose influencing factors included age, weight gain during pregnancy, and newborn weight. CONCLUSION: Effects on surface electromyography (sEMG) of pelvic floor muscles (PFMs) at 6-8 weeks postpartum differed based on the different modes of delivery. The high-risk obstetric factors closely related to abnormal surface electromyography (sEMG) of pelvic floor muscles (PFMs) were maternal age, height, weight, and second stage of labor.

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Acquired Brain Injuries are one of the leading causes of mortality and disability worldwide. One of the most frequent sequelae is motor impairment of the upper limbs, which affects people's functionality and quality of life. Following the discovery of mirror neurons, new techniques were developed based on the mechanisms of neuronal plasticity, such as motor imagery (MI) and action observation (AO). We propose a protocol using electromyographic recordings of forearm muscles in people who have suffered a stroke during an MI task and an AO task. Three different experimental conditions will be studied during the electromyographic recordings: control recording, recording during MI, and recording during AO. Understanding the muscle activation in each technique will allow us to develop future protocols and intervention plans, improving the quality of care for people who have suffered a stroke.

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In this paper, surface electromyography (sEMG) is used to gather the activation neural signal from muscles during an indoor rowing exercise. The exercise was performed by professional athletes and amateur non-athletes. The data acquisition and processing are described to obtain a set of parameters: number of cycles, average cycle time, cycle time standard deviation, fatigue time, muscle activation time, and muscle energy. These parameters are used to draw conclusions on common non-athletes' mistakes during exercise for better training advice and a way of statistically distinguishing an athlete from a non-athlete.

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OBJECTIVES: To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition. METHODS: The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset. RESULTS: The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924. CONCLUSIONS: The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition.

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OBJECTIVE: Spinal endoscopy radiofrequency is a minimally invasive technique for lumbar disc herniation (LDH) and low back pain (LBP). However, recurring LDH/LBP following spinal endoscopy radiofrequency is a significant problem. Paravertebral musculature plays a crucial role in spine stability and motor function, and the purpose of the present study was to identify whether patients' baseline lumbar muscular electrophysiological function could be a predictor of recurring LDH/LBP. METHODS: This was a prospective follow-up and case-control study focusing on elderly patients with LDH who were treated in our department between January 1, 2018, and October 31, 2021. The end of follow-up was recurring LBP, recurring LDH, death, missing to follow-up or 2 years postoperation. The surface electromyography test was performed before the endoscopy C-arm radiofrequency (ECRF) operation to detect the flexion-relaxation ratio (FRR) of the lumbar multifidus (FRRLM ) and the longissimus erector spinae (FRRES ), and the other baseline parameters included the general characteristics, the visual analogue scale, the Japanese Orthopaedic Association score, and the Oswestry Disability Index. Intergroup comparisons were performed by independent t-test and χ2 -test, and further binary logistic regression analysis was performed. RESULTS: Fifty-four patients completed the 2-year follow-up and were retrospectively divided into a recurring LDH/LBP group (Group R) (n = 21) and a no recurring group (Group N) (n = 33) according to their clinical outcomes. FRRLM and FRRES in Group N were much higher than those in Group R (p < 0.001, p = 0.009). Logistic regression analysis showed that only the FRRLM (odds ratio [OR] = 0.123, p = 0.011) and FRRES (OR = 0.115, p = 0.036) were independent factors associated with the ECRF outcome. CONCLUSIONS: Lumbar disc herniation patients' baseline FRRLM and FRRES are independent outcome predictors of recurring LDH/LBP after ECRF. For every unit increase in baseline FRRLM , the risk of recurring LDH/LBP is decreased by 87.7%, and for every unit increase in baseline FRRES , the risk of recurring LDH/LBP is decreased by 88.5%.

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Nowadays, surface electromyography (sEMG) is evolving as a technology for hand gesture recognition. Detailed studies have revealed the capacity of EMG signals to access detailed information, particularly in the classification of hand gestures. Indeed, this advancement emerges as an interesting element in refining the recognition and interpretation of sign languages and exploring deeper into the phonology of signed languages. Aligned with this advancement and the need for a reliable and mobile sign language recognition system, we introduce a specialized sEMG dataset, acquired using the Myo armband. This device is adept at capturing recordings at frequencies of up to 200 Hz. The dataset focuses on the 28 letters of the Arabic alphabet and 10 digits using hand gestures, with each gesture captured into 400 frames. This considerable collection of 18,716 samples was achieved with the cooperation of three contributors, providing a varied and comprehensive range of gestural data.

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OBJECTIVE: To explore the effects of trunk training using motor imagery on trunk control and balance function in patients with stroke. METHODS: One hundred eligible stroke patients were randomly divided into a control group and trial group. The control group was given routine rehabilitation therapy, while the trial group was given routine rehabilitation therapy and trunk training using motor imagery. RESULTS: Prior to treatment, there was no significant difference between the two groups (P > 0.05) in Sheikh's trunk control ability, Berg rating scale (BBS), Fugl-Meyer assessment (FMA), movement length, movement area, average front-rear movement speed, average left-right movement speed, and surface electromyography (sEMG) signal of the bilateral erector spinae and rectus abdominis. After treatment, Sheikh's trunk control ability, FMA, and BBS in the two groups were significantly higher than those before treatment (P < 0.05). The movement length, movement area, the average front-rear movement speed, and the average left-right movement speed in the two groups decreased significantly (P < 0.05). The differences of these indicators between the two groups were statistically significant (P < 0.05). After treatment, the rectus abdominis and erector spinae on the affected side of the two groups improved when compared with those before treatment (P < 0.05). The rectus abdominis and erector spinae on the healthy side of the trial group descended after treatment (P < 0.05), while little changes were observed on the healthy side of the control group after treatment (P > 0.05). The rectus abdominis and erector spinae on the affected side of the trial group improved when compared with those in the control group (P < 0.05). There was no significant difference between the two groups in the decline of abdominalis rectus and erector spinal muscle on the healthy side. CONCLUSION: Trunk training using motor imagery can significantly improve the trunk control ability and balance function of stroke patients and is conducive to promoting the recovery of motor function.

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Estimation of continuous motion of human joints using surface electromyography (sEMG) signals has a critical part to play in intelligent rehabilitation. Traditional methods always use sEMG signals as inputs to build regression or biomechanical models to estimate continuous joint motion variables. However, it is challenging to accurately estimate continuous joint motion in new subjects due to the non-stationarity and individual differences in sEMG signals, which greatly limits the generalisability of the method. In this paper, a continuous motion estimation model for the human knee joint with a parameter self-updating mechanism based on the fusion of particle swarm optimization (PSO) and deep belief network (DBN) is proposed. According to the original sEMG signals of different subjects, the method adaptively optimized the parameters of the DBN model and completed the optimal reconstruction of signal feature structure in high-dimensional space to achieve the optimal estimation of continuous joint motion. Extensive experiments were conducted on knee joint motions. The results suggested that the average root mean square errors (RMSEs) of the proposed method were 9.42° and 7.36°, respectively, which was better than the results obtained by common neural networks. This finding lays a foundation for the human-robot interaction (HRI) of the exoskeleton robots based on the sEMG signals.

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In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human-robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R2 of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer's motion intentions in human-robot collaboration control.

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Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person's intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1-20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time-frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

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Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

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Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion: As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost.

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sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method.

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Background: The patients with chronic low back pain (CLBP) showed impaired postural control, especially in challenging postural task. The dorsolateral prefrontal cortex (DLPFC) is reported to involve in the complex balance task, which required considerable attentional control. The effect of intermittent theta burst stimulation (iTBS) over the DLPFC to the capacity of postural control of CLBP patients is still unknown. Methods: Participants diagnosed with CLBP received a single-session iTBS over the left DLPFC. All the participants completed the postural control tasks of single-leg (left/right) standing before and after iTBS. The activation changes of the DLPFC and M1 before and after iTBS were recorded by functional near-infrared spectroscopy (fNIRS). The activation pattern of the trunk [transversus abdominis (TrA), superficial lumbar multifidus (SLM)] and leg [tibialis anterior (TA), gastrocnemius medialis (GM)] muscles including root mean square (RMS) and co-contraction index (CCI) during single-leg standing were measured by surface electromyography (sEMG) before and after the intervention. The paired t-test was used to test the difference before and after iTBS. Pearson correlation analyses were performed to test the relationship between the oxyhemoglobin concentration and sEMG outcome variables (RMS and CCI). Results: Overall, 20 participants were recruited. In the right-leg standing condition, compared with before iTBS, the CCI of the right TrA/SLM was significantly decreased (t = -2.172, p = 0.043), and the RMS of the right GM was significantly increased (t = 4.024, p = 0.001) after iTBS. The activation of the left DLPFC (t = 2.783, p = 0.012) and left M1 (t = 2.752, p = 0.013) were significantly decreased and the relationship between the left DLPFC and M1 was significant after iTBS (r = 0.575, p = 0.014). Correlation analysis showed the hemoglobin concentration of M1 was negatively correlated with the RMS of the right GM (r = -0.659, p = 0.03) and positively correlated between CCI of the right TrA/SLM (r = 0.503, p = 0.047) after iTBS. There was no significant difference in the brain or muscle activation change in the left leg-standing condition between before and after iTBS. Conclusion: Intermittent theta burst stimulation over the left DLPFC seems to be able to improve the muscle activation pattern during postural control ability in challenging postural task, which would provide a new approach to the treatment of CLBP.

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A randomized crossover trial was designed to investigate the influence of muscle activation and strength on functional stability/control of the knee joint, to determine whether bilateral imbalances still occur six months after successful anterior cruciate ligament reconstruction (ACLR), and to analyze whether the use of orthotic devices changes the activity onset of these muscles. Furthermore, conclusions on the feedforward and feedback mechanisms are highlighted. Therefore, twenty-eight patients will take part in a modified Back in Action (BIA) test battery at an average of six months after a primary unilateral ACLR, which used an autologous ipsilateral semitendinosus tendon graft. This includes double-leg and single-leg stability tests, double-leg and single-leg countermovement jumps, double-leg and single-leg drop jumps, a speedy jump test, and a quick feet test. During the tests, gluteus medius and semitendinosus muscle activity are analyzed using surface electromyography (sEMG). Motion analysis is conducted using Microsoft Azure DK and 3D force plates. The tests are performed while wearing knee rigid orthosis, soft brace, and with no aid, in random order. Additionally, the range of hip and knee motion and hip abductor muscle strength under isometric conditions are measured. Furthermore, patient-rated outcomes will be assessed.

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The magnitude and duration of muscle force production are influenced by the fiber type proportion. In this work, surface electromyography (sEMG) signals of muscles with varied fiber type proportions, are generated. For this, relevant components of existing models reported in various literature have been adopted. Also, a method to calculate the motor unit size factor is proposed. sEMG signals of adductor pollicis (AP) and triceps brachii (TB) muscles are simulated from the onset of force production to muscle fatigue state at various percentages of maximal voluntary contraction (MVC) values. The model is validated using signals recorded from these muscles using well-defined isometric exercise protocols. Root mean square and mean power spectral density values extracted from the simulated and recorded signals are found to increase for TB and decrease for AP with time. A linear variation of the features with %MVC values is obtained for simulated and experimental results. The Bland-Altman plot is used to analyze the agreement between simulated and experimental feature values. Good agreement is obtained for the feature values at various %MVCs. The mean endurance time calculated using the model is found to be comparable to that of the experimental value. This method can be used to generate sEMG signals of different muscles with varying fiber type ratios under various neuromuscular conditions.

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Objective To simultaneously collect and analyze the kinematic and dynamic parameters for two techniques of traditional Chinese cervical manipulation ( TCCM), and quantitatively describe its biomechanical characteristics. Methods A senior practitioner completed the TCCM (positioning and directional rotation pulling, lateral flexion, respectively) on 10 healthy subjects, and the fluorescent marker balls were pasted on the operator to capture manipulation movements. The dynamic parameters and the surface electromyography ( sEMG) signals were collected by pressure-sensitive gloves and wireless sEMG acquisition system. Results The upper arm muscle was the main force muscle during TCCM, and biceps brachii had the highest contribution rate. The range of motion (ROM), speed, pulling force, and time during cervical spine positioning and directional rotation pulling were all greater than those during cervical spine lateral flexion. The integrate electromyography ( iEMG) and root mean square (RMS) for each muscle of the operator during cervical spine positioning and directional rotation pulling were higher than those during cervical spine lateral flexion. Conclusions The overall ROM, three-dimensional (3D) motion angle, load intensity and time during CCTM have the characteristics of high speed, low amplitude and strong force, reflecting the biomechanical characteristics of ‘ cunjin ’ ( one-inch punch ) in traditional Chinese medicine. This study provides references for further standardizing manual teaching and training and improving clinical safety.