Electromyography Signals Research Articles

Three-dimensional finite element analysis of biological signal feedback in the mechanical properties of sound production

In response to the problems of low reliability and long time consumption in traditional research on sound production, this article used electromyography (EMG) signals as input signals to build a high-precision sound production mechanics model. A Proportional-Integral-Derivative (PID) controller was used to dynamically adjust the model and develop a real-time feedback system. This article established a detailed three-dimensional (3D) finite element model including the vocal cords and throat, defined the nonlinear elastic properties and linear elastic properties of different tissues, and used tetrahedral grid partitioning technology to improve the computational accuracy of the model. Through a EMG sensor, an individual EMG was collected and filtered to remove noise. The processed EMGs were used as input parameters for the finite element model to drive the muscle units in the model. By using a PID controller to receive real-time EMG input, the error was calculated and the model was adjusted, enabling accurate simulation of the mechanical properties of vocal cord vibration under different vocal states and achieving real-time feedback. Considering the complexity of vocal cord vibration driven by biological signals, this article simulated and analyzed the modal characteristics of vocal cord vibration, and analyzed the differences in vocal cord vibration characteristics under different vocal states. The sound pressure distribution and resonance frequency were simulated and analyzed to understand the propagation characteristics of sound. Finally, by comparing and analyzing the simulated data with the actual collected data, it was found that the maximum relative error rate of the model under different sound states was 6.14%, and the overall error rate of the model was relatively low, which verified the reliability of the model. The findings demonstrated that the feedback delays of the model in different sound states were all within 100 milliseconds, indicating that the system had high real-time performance and accuracy, which was promising in practical applications.

A comparison of electromyography techniques: surface versus intramuscular recording.

This review is a comprehensive guide for electromyography (EMG) researchers, providing a comparison of skin EMG recording (surface EMG: sEMG and high-density sEMG: HD-sEMG) and intramuscular EMG recording (multi-motor unit-MMU and single motor unit electromyography-SMU). We delve into the nuances of techniques, highlighting their strengths and limitations in quantifying muscle activation during dynamic and static conditions. We first examine how EMG signals change with time, focussing on the interplay between motor unit synchronisation and signal amplitude. The review then explores the impact of electrode placement on signal quality. We further discuss the challenges of signal cancellation, crosstalk from neighbouring muscles, and motion artifacts, which can significantly affect signal integrity. Finally, we address the temporal changes in electrode impedance and its implications for data interpretation. Our analysis proposes that specific research objectives should guide the choice amongst sEMG, HD-sEMG, SMU and MMU. MMU, which records the peak counts of individual motor unit action potentials from a localised muscle area, is particularly suited for studying deep or small muscles during static and dynamic activities. Its high sensitivity to motor unit recruitment and discharge rates minimises the impact of factors such as signal cancellation and motion artefacts. Conversely, sEMG is well-suited for short-duration, isometric assessments of large, superficial muscles. HD-sEMG helps study single motor unit properties under isometric conditions. SMU is particularly suited for studying neuronal networks between stimulated sites and motor neurons. This review aims toprovideresearchers with the information to select the most appropriate EMG technique for their investigations.

Effect of fatigue on intermuscular EMG-EMG coupling during bench press exercise at 60% 1RM workload in males.

To explore the neuromuscular control mechanism and quantifying the fatigue response during bench press exercise is important references to prescribe an appropriate exercise program. However, current literature struggles to provide a concrete conclusion on the changes of intermuscular EMG-EMG coupling between synergistic and antagonist muscles during the exercise. Thus, the current study was designed to reveal fatigue-related changes of intermuscular EMG-EMG coupling during bench press exercise. Thirty-one healthy male participants performed a bench press exercise on the Smith machine at 60% One Repetition Maximum (1RM) workload to exhaustion, while surface electromyographic signals (sEMG) were collected from triceps brachii (TB), biceps brachii (BB), anterior deltoid (AD), posterior deltoid (PD), and pectoralis major (PM). Surface EMG signals were divided into the first half and second half of the bench press exercise. Phase synchronization index (PSI) was calculated between sEMG of synergistic muscle pairs AD-TB, AD-PM and antagonist muscle pairs BB-TB, AD-PD. EMG power of TB, AD, PD, PM muscles in alpha (8-12 Hz) frequency band and EMG power of each muscle in beta (15-35 Hz), and gamma (35-60 Hz) frequency bands were all increased during the second half of contraction compared with the first half of contraction. PSI of gamma frequency band was significantly decreased in BB-TB muscle pair while EMG-EMG coupling of AD-TB in gamma frequency band was significantly increased during the second half of contraction compared to the first half of contraction. The results indicated a decrease of interconnection between synchronized cortical neurons and the motoneuron pool of BB and TB, and an increase of interconnection between AD-TB muscles during fatiguing bench press exercise at 60% 1RM workload. The changes of intermuscular coupling may be related to the supraspinal modulations to compensate for the decrease of muscle force as well as a result of unbalanced changes of agonist and antagonist muscle contractility.

A Machine Learning–Based Ergonomic Assessment of Wireless Hand Control System for Lower‐Limb Disabled Tractor Operators and Abled Female Agricultural Workers

ABSTRACTTractor being the most used power source for agricultural operations needs hand control (HC) and foot control (FC) to maneuver it. FCs restrict lower‐limb disabled agricultural workers from participating in tractor operation, and high requirement of actuation forces to operate FCs may create overexertion and early fatigue to female agricultural workers. Therefore, a sensor‐based HC system has been developed to assist them in tractor operation with minimal actuating force. This study focuses on ergonomic assessment of the HC system to assess the suitability for the abled and disabled agricultural workers, including physiological, psychophysical, and muscle fatigue parameters. Heart rate (HR) of abled male and female, and disabled male and female was observed in the range of 83–118, 85–117, 93–118, and 92–114 beats/min, respectively, during tractor operation. Energy expenditure rate (EER) during tractor operation with FCs (9.7–17.4 kJ/min) was observed higher than with the HC system (7.3–16.5 kJ/min). Body parts discomfort was observed highest for the right hand of all the subjects (4.9–5.3) and maximum overall discomfort was experienced by abled females during the operation with FCs (5.4) as they have to exert higher force. The root mean square (RMS) value of the electromyography signal obtained for extensor digitorum muscle was found to be higher for all the subjects and with both HC and FC (abled male, 17.37–40.43 µV; abled female, 14.76–45.29 µV; disabled male, 15.49–40.23 µV; disabled female, 30.32–54.29 µV) than other upper arm muscles middle deltoid, flexor carpi radialis, and brachioradialis. Muscle workload for all the selected muscles of all the subjects was observed within the recommended limit during the tractor operation with a developed HC system (< 30%). Categorization of overall discomfort rating (ODR) of the subjects using HR, EER, and RMS through machine learning algorithms such as k‐nearest neighbor (KNN), random forest classifier, and support vector machine predicted the ODR with accuracies in the range of 77%–83%. KNN algorithm was found to be most accurate with prediction accuracy of 83%. The developed HC system provides assistantship to the lower‐limb disabled agricultural workers (1%–100% disability of lower limbs) and allows female workers to operate the tractor with minimal physical exertion.

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

Enhancing gait recognition in lower limb exoskeletons: adaptive feature selection and random forest with Bayesian optimization

Abstract To improve human-machine cooperation and enhance the accuracy of gait recognition in wearable lower limb exoskeletons, an enhancement method of gait recognition based on adaptive feature selection and an optimized machine learning algorithm was proposed. In this study, surface electromyography (sEMG) signals of rectus femoris, medialis femoris, lateralis femoris, semitendinosus and biceps femoris were recorded during level-ground walking. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features, and the mean power frequency and median frequency were selected as the frequency domain features. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. To reduce feature dimension and improve the calculation efficiency, adaptive feature selection was performed by particle swarm optimization combined with sigmoid function. Then, the feature matrix was determined as the input for a random forest classifier to recognize different gait phases. An adaptive optimization mechanism based on Bayesian optimization was applied to random forest. Compared with random forest, the overall performance of the optimized model was improved. Its accuracy was increased by 3.57%. The feature selection and adaptive optimization mechanisms in gait recognition not only enhance the accuracy of random forest algorithms applied to sEMG for gait prediction, but also facilitate the flexibility and consistency required for lower limb exoskeleton gait control.

Design and analysis of exoskeleton devices for rehabilitation of distal radius fracture.

In this work, the mechanical principles of external fixation and resistance training for the wrist affected by a distal radius fracture (DRF) are revealed. Based on the biomechanical analysis, two wearable exoskeleton devices are proposed to facilitate the DRF rehabilitation progress. Chronologically, the adjustable fixation device (AFD) provides fixed protection and limited mobilization of the fractured wrist in the early stage, while the functional recovery of relevant muscles is achieved by the resistance training device (RTD) in the later stage. According to the designed mechatronic systems of AFD and RTD, the experimental prototypes for these two apparatuses are established. By experiments, the actual motion ranges of AFD are investigated, and the feasibility in monitoring joint angles are validated. Meanwhile, the resistant influences of RTD are analyzed based on the surface electromyography (sEMG) signal features, the results demonstrate that the training-induced muscle strength enhancement is generally increased with the increment in external resistance. The exoskeleton devices presented in this work would be beneficial for the active rehabilitation of patients with DRF.

Silk fibroin-based bioelectronic devices for high-sensitivity, stable, and prolonged in vivo recording

Silk fibroin, recognized for its biocompatibility and modifiable properties, has significant potential in bioelectronics. Traditional silk bioelectronic devices, however, face rapid functional losses in aqueous or in vivo environments due to high water absorption of silk fibroin, which leads to expansion, structural damage, and conductive failure. In this study, we developed a novel approach by creating oriented crystallization (OC) silk fibroin through physical modification of the silk protein. This advancement enabled the fabrication of electronic interfaces for chronic biopotential recording. A pre-stretching treatment of the silk membrane allowed for tunable molecular orientation and crystallization, markedly enhancing its aqueous stability, biocompatibility, and electronic shielding capabilities. The OC devices demonstrated robust performance in sensitive detection and motion tracking of cutaneous electrical signals, long-term (over seven days) electromyographic signal acquisition in live mice with high signal-to-noise ratio (SNR >20), and accurate detection of high-frequency oscillations (HFO) in epileptic models (200–500 Hz). This work not only improves the structural and functional integrity of silk fibroin but also extends its application in durable bioelectronics and interfaces suited for long-term physiological environments.

Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) sensors.Design/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for classification.FindingsThe findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification accuracy.Practical implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that neglecting DRA can lead to long-term issues such as back pain, pelvic floor dysfunction, and compromised core strength.Social implicationsDuring our data collection camps, there were educational sessions to raise awareness about the DRA problem and the benefits of rehabilitation exercises. This dual approach helped in building trust and encouraging participation. Moreover, the use of wearable sensors in this study provided a non-invasive and convenient way for new mothers to engage in rehabilitation exercises without needing frequent visits to a clinic, which is often impractical for them.Originality/valueThe utilization of discriminating features retrieved from the output layer of 1D CNN is a significant contribution to this work. The responses of this study indicate that 1D convolutional neural network (1D CNN) and Boosting algorithms used in a transfer learning strategy produce successful discrimination between accurate and inaccurate performance of exercises by achieving an accuracy of 96%.

A proportional fusion adaptation algorithm to reduce noise in sEMG signals of the lower limb

Abstract To address issues related to unknown noise pollution and the inadequate performance of single adaptive noise reduction algorithms in measuring lower limb surface electromyography (sEMG) signals, this paper proposes an algorithm that combines the fast convergence speed and strong noise reduction capability of recursive least-squares (RLS) with the low computational complexity of the normalized least-mean-square (NLMS) and improved proportionate NLMS (IPNLMS). Through the proportional fusion of RLS with NLMS and RLS with IPNLMS, the proposed algorithm greatly improves convergence speed, stability, and noise reduction performance while effectively reducing computational complexity. Moreover, considering the influence of the initial value of weights on the noise reduction performance during the updating process of the adaptive noise reduction algorithm, a weight initial value setting (WIS) module is proposed to optimize the initial value of the weights by the known amount of data. Based on 50 independent experiments, an adaptive noise reduction algorithm and WIS module were used to reduce the unknown noise in the lower limb sEMG signals, which was generated by white Gaussian noise, power line interference, or hybrid noise interfered by an unknown environment. The noise reduction performance was evaluated by using the average value of the signal-to-noise ratio (SNR), the root-mean-square error, and R-square. Compared with the RLS, NLMS, and IPNLMS algorithms for noise reduction of vastus lateralis, rectus femoris, and biceps femoris signals containing unknown noise, the SNR of RLS-NLMS, RLS-IPNLMS, WIS-RLS-NLMS, and WIS-RLS-IPNLMS is improved by an average of [7.92%, 55.54%, 55.63%], [7.45%, 54.90%, 54.99%], [19.70%, 72.38%, 72.71%], [19.32%, 71.84%, 72.19%]. The simulation results verify that the proportional fusion adaptive noise reduction algorithm and the WIS module effectively accelerate the convergence speed, enhance the noise reduction capability, and reduce the computational complexity.

Comparative Analysis of Linear and Nonlinear sEMG Methods for Detecting Muscle Fatigue During Dynamic Biceps Curls

Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established linear and nonlinear methods in measuring muscle fatigue caused by dynamic contractions through surface electromyography (sEMG) signals. A group of twelve healthy individuals completed biceps curls at a consistent pace of one repetition per four seconds, which constituted 75% of their 10-repetition maximum. Concurrently, sEMG signals were captured from the biceps brachii muscle at 1000 Hz. To assess the sEMG signals during the initial, middle, and final sets of 10 repetitions, three linear metrics—mean frequency, median frequency, and spectral moment ratio (SMR)—along with two nonlinear approaches, namely sample entropy and detrended fluctuation analysis (DFA), were utilized. The study's outcomes indicated notable shifts in the SMR values and the two DFA-derived scaling exponents across the exercise sets. These results indicated that SMR, sample entropy, and DFA are effective in gauging muscle fatigue, with sample entropy and DFA demonstrating heightened sensitivity to the fatigue levels when compared to the linear metrics.

A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing

Neuromorphic computing devices offer promising solutions for next‐generation computing hardware, addressing the high throughput data processing demands of artificial intelligence applications through brain‐mimicking non‐von Neumann architecture. Herein, PEDOT:Tos/PTHF‐based organic electrochemical transistors (OECTs) with dual‐modal memory functions—both short‐term and long‐term—are demonstrated. By characterizing memory levels and relaxation times, the device has been efficiently manipulated and switched between the two modes through coupled control of pulse voltage and duration. Both short‐term and long‐term memory functions are integrated within the same device, enabling its use as artificial neurons for the reservoir unit and synapses in the readout layer to build up a reservoir computing (RC) system. The performance of the dynamic neuron and synaptic weight update are benchmarked with classification tasks on hand‐written digit images, respectively, both attaining accuracies above 90%. Furthermore, by modulating the device as both reservoir mode and synaptic mode, a full‐OECT RC system capable of distinguishing electromyography signals of hand gestures is demonstrated. These results highlight the potential of simplified, homogeneous integration of dual‐modal OECTs to form brain‐like computing hardware systems for efficient biological signal processing across a broad range of applications.

Thyroid Radiofrequency Ablation-Thermal Effects on Recurrent Laryngeal Nerve Using Continuous Intraoperative Neuromonitoring Animal Model.

When performing radiofrequency ablation for thyroid nodules, it is essential to avoid thermal injury to the recurrent laryngeal nerve. This porcine animal model study used continuous intraoperative neuromonitoring to investigate the thermal safety parameters of thyroid radiofrequency ablation. Porcine animal study. University animal laboratory. Twelve piglets were tested at different radiofrequency power levels, and the real-time electromyography signal changes were recorded under continuous intraoperative neuromonitoring. The spread heat study (8 piglets) included spontaneous recovery tests and cold water irrigation tests to investigate the safety distance from the recurrent laryngeal nerve to the active tip during 5-second activation with standard stimulation patterns. The residual heat study (4 piglets) investigated the safety cooling durations by touching the recurrent laryngeal nerve with the tip after a 5-second activation. In the spread heat study, substantial signal attenuation events were observed at an spread heat distance of 2, 3, 5, and 5 mm when the power was set as 10, 20, 30, and 50 W, respectively. No signal recovery could be observed in 20 minutes with or without cold water irrigation in the injured recurrent laryngeal nerve area. The residual heat study shows the residual thermal effect of the tip is minimal, and no substantial signal attenuation event was observed at all experiments. This innovative study established the thermal safety parameters for radiofrequency ablation in a porcine model at various power levels, which can potentially assist operators in delineating a precise ablation field and providing effective thyroid ablation treatment safely.

Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model.

Human-machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects' performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements.

Dynamics of cardio-muscular networks in exercise and fatigue.

A fundamental question in cardiovascular and muscle physiology is how the heart operates in synchrony with distinct muscles to regulate homeostasis, enable movement and adapt to exercise demands and fatigue. Here we investigate how autonomic regulation of cardiac function synchronizes and integrates as a network with the activity of distinct muscles during exercise. Further, weestablish how the network of cardio-muscular interactions reorganizes with fatigue. Thirty healthy young adults performed two body weight squat tests until exhaustion. Simultaneous recordings were taken of a 3-lead electrocardiogram (EKG) along with electromyography (EMG) signals from the left and right vastus lateralis, and left and right erector spinae. We first obtained instantaneous heart rate (HR) derived from the EKG signal and decomposed the EMG recordings in 10 frequency bands (F1-F10). We next quantified pair-wise coupling (cross-correlation) between the time series for HR and all EMG spectral power frequency bands in each leg and back muscle. We uncovered the first profiles of cardio-muscular network interactions, which depend on the role muscles play during exercise and muscle fibre histochemical characteristics. Additionally, we observed a significant decline in the degree of cardio-muscular coupling with fatigue, characterized by complex transitions from synchronous to asynchronous behaviour across a range of timescales. The network approach we utilized introduces new avenues for the development of novel network-based markers, with the potential to characterize multilevel cardio-muscular interactions to assess global health, levels of fatigue, fitness status or the effectiveness of cardiovascular and muscle injury rehabilitation programmes. KEY POINTS: The heart operates in synchrony with muscles to regulate homeostasis, enable movement, and adapt to exercise demands and fatigue. However, the precise mechanisms regulating cardio-muscular coupling remain unknown. This study introduces a pioneering approach to assess cardio-muscular network interactions by examining the synchronization of cardiac function with muscle activity during exercise and fatigue. We uncover the first profiles of cardio-muscular interactions characterized by specific hierarchical organization of link strength. We observe a significant decline in the degree of cardio-muscular coupling with fatigue, marked by complex transitions from synchronous to asynchronous behaviour. This network approach offers new network-based markers to characterize multilevel cardio-muscular interactions to assess global health, levels of fatigue, fitness status or the effectiveness of cardiovascular and muscle injury rehabilitation programmes.

Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience.

Many studies have investigated the manipulation of a virtual upper arm using electromyogram (EMG); however, these studies primarily used a machine learning model or trigger control for this purpose. Furthermore, most of them could only display the constant motion of the virtual arm because the motion to be displayed was selected by pattern recognition or trigger control. In addition, these studies did not examine changes in the electromyographic signals after experiencing the virtual arm. By contrast, we propose a real-time, continuous, learning-free avatar that manipulates the virtual arm with electromyogram signals or physio-avatar EMG biofeedback (EB). The goal of the physio-avatar EB system is to induce physiological changes through experiential interactions. We explored the possibility of changing motor control strategies by applying the system to healthy individuals as a case study. An intervention method that provided an experience of a body different from one's own was conducted on seven participants using a time-invariant calculation algorithm to determine the joint angles of the avatar. Control strategies for an indicator of the equilibrium point in the baseline and adaptation phases were determined to evaluate the physio-avatar EB intervention effect. The similarity of these BL and adaptation control strategies compared to those used during the washout period was assessed using the coefficient of determination. The accuracy and reliability of the virtual reality (VR) system were evaluated by comparison with existing studies and the required specs. Changes in motor control strategies due to the physio-avatar EB system were observed in four experiments, where the participants gradually returned to their pre-intervention control strategies. This result can be attributed to the aftereffects caused by error learning. This implies that the developed system influenced their motor control strategies. The number of EMG acquisition bits was 16 bits, and the sampling rate was 1,000 Hz. The refresh rate of the head-mounted display was 90 Hz, and its resolution was for a single eye. Additionally, the simulation frame rate was 30 FPS. These values were adequate compared to existing studies and required specs. The essential contribution of this study is the development of an avatar that is controlled by a different method than has been used in previous studies and the demonstration of changes in a subject's muscle activity after they experience an avatar. In the future, the clinical efficacy of the proposed system will be evaluated with actual patients.

Design and Construction of 5-Dof EMG Based Robotic Arm System

Electromyography (EMG) is an alternate method of obtaining muscle signal outputs. As a result, the current development in designing my electric plans has piqued the attention of researchers in this subject. This is because Standard controllers lack essential components, limiting the use of limbs to operate equipment, namely an arm controlled by robotics. EMG signals are subject to noise, including crosstalk, motion artifacts, ambient noise, and intrinsic noise. Electromyography preparation requires careful selection of muscle groups, electrode placement, and environment quality, all of which impact the signal output. The goal of this study is to create an EMG-based robotic arm control system that can be used to help the elderly, individuals with impairments, and those who operate in dangerous places. Initially, a literature review on current human-robot interaction approaches and analysis of the kinematics of a 5 DOF robotic arm has been conducted. Then, utilizing Electromyogram (EMG) data obtained from the muscles of the elbow, away for controlling a 5 DOF robotic arm is provided. Two accelerometers are utilized to record the human arm's gesture and posture, this information is then communicated to the robotic arm as an input. In the experiments, wrist rotation, left, right, up, and down movements were used to establish controlled motion of a 5 DOF robotic arm. The project's goal has been met with the successful development of an EMG-controlled robotic arm. The robotic arm may yet be improved by including Implementing a wireless sensor network with multiple channels.

Alterations in magnitude and spatial distribution of erector spinae muscle activity in cyclists with a recent history of low back pain.

While cycling offers several health benefits, repetitive loading and maintenance of static postures for prolonged periods expose cyclists to low back pain (LBP). Despite high LBP prevalence in cyclists, underlying pathomechanics and specific lumbar region muscle activation patterns during cycling are unclear. Here, we compared lumbar erector spinae (ES) muscles activation and spatial distribution activity in cyclists with and without recent LBP history. Ten cyclists with recent LBP history (LBPG; Oswestry Disability Index score ~ 17.8%) and 11 healthy cyclists (CG) were recruited. After assessing the Functional Threshold Power (FTP), participants underwent an incremental cycling test with 4 × 3min steps at 70%, 80%, 90%, and 100% of their FTP. High-density surface electromyography (HDsEMG) signals were recorded from both lumbar ES using two 64-channel grids. Information about ES activation levels (root-mean-square, RMS), degree of homogeneity (entropy), and cranio-caudal displacement of muscle activity (Y-axis coordinate of the barycenter of RMS maps) was extracted from each grid separately and then grand-averaged across both grids. Repeated-measure 2-way ANOVAs showed a significant intensity by group interaction for RMS amplitude (p = 0.003), entropy (p = 0.038), and Y-bar displacement (p = 0.033). LBPG increased RMS amplitude between 70-100% (+ 19%, p = 0.010) and 80-100% FTP (+ 21%, p = 0.004) and decreased entropy between 70-100% FTP (-8.4%, p = 0.003) and 80-100% FTP (-8.5%, p = 0.002). Between-group differences emerged only at 100% FTP (+ 9.6%, p = 0.049) for RMS amplitude. Our findings suggest that cyclists with recent LBP history exhibit higher ES muscles activation and less homogeneous activity compared to healthy controls, suggesting potential inefficient muscle recruitment strategy. HEC-DSB/09-2023.

Design and development of an EMG controlled transfemoral prosthesis

Electromyography (EMG) signals are biomedical signals that measure electrical currents generated by the activity of muscles when they contract. EMG is essential for optimizing the control of various prosthetic devices, particularly for transfemoral amputees, where the complexity of muscle signal integration presents significant challenges. The proposed study aims to develop a prosthetic knee that actuates in real-time using the EMG signals from the amputee’s residual limb. Pre-processing techniques are employed to obtain EMG signals from the femoris and vastus muscle targets in the transfemoral region. Moving average filters and Butterworth bandpass filters are implemented to process the raw signals. Sliding windows of various widths were applied for feature extraction. The window size of 200 ms is determined for our study based on the outcomes of the t-SNE plots and the corresponding silhouette scores. After the extraction of the pertinent features, several supervised classifier algorithms are put into practice to classify the knee flexion and extension motion. The k-nearest Neighbor (KNN) algorithm, with an accuracy rating of 80 %, proved to be suitable for motor control. Real-time control is implemented using the Raspberry Pi board to power the prosthesis allowing above-the-knee amputees to voluntarily move the leg back and forth. The EMG signals are then extracted and used to drive the DC motor. The prosthesis would therefore be able to move more precisely since the EMG readings are being gathered in real-time. Thus, this work can enhance the patient’s comfort with the ease of carrying out knee movements.

How does prolonged tennis playing affect lower limb muscles' activity during first and second tennis serves?

We examined the effect of prolonged tennis playing on lower limb muscles' activity during the execution of first and second tennis serves. Ten male competitive tennis players executed five first and second serves before (pretest) and after (posttest) a 3-h tennis match. Surface electromyographic (EMG) activity of four lower limb muscles (vastus lateralis, rectus femoris, gastrocnemius lateralis, and soleus muscles) on each leg was recorded along with maximum ball velocity measured by a radar gun and peak vertical forces recorded by a force platform. For the vastus lateralis, gastrocnemius lateralis, and soleus muscles of the left leg as well as the vastus lateralis muscle of the right leg, EMG amplitude decreased from pre- to posttests (p≤0.033). These reductions in the EMG signal were generally more pronounced in the first serve (i.e., ranging from -10% to -40%) compared to the second serve (0% to -25%). Maximum ball velocity for both first (159±12 vs. 154±12km/h) and second (126±20 vs. 125±15km/h) serves remained unchanged from pre- to posttests (p=0.638) Similarly, peak vertical forces did not differ between pretest and posttest for both first (1.78±0.30 vs. 1.72±0.29 body weight) and second (1.62±0.25 vs. 1.75±0.23 body weight) serves (p=0.730). In conclusion, a 3-h tennis match led to decreased activation levels in various leg muscles during serves, particularly in first serves compared to second serves. Despite consistent maximum ball velocity and peak vertical forces, these reductions in EMG signals suggest that skilled tennis players may adopt compensatory strategies after prolonged play.