Surface Electromyography Data Research Articles

The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals.

Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving precise hand gesture recognition using surface electromyography signals is crucial due to the complexity and variability of surface electromyography data. Advanced signal processing and data analysis techniques are required to effectively extract meaningful information from these signals. In our study, we utilized three datasets: NinaPro Database 1, CapgMyo Database A, and CapgMyo Database B. These datasets were chosen for their open-source availability and established role in evaluating surface electromyography classifiers. Hand gesture recognition using surface electromyography signals draws inspiration from image classification algorithms, leading to the introduction and development of the Novel Signal Transformer. We systematically investigated two feature extraction techniques for surface electromyography signals: the Fast Fourier Transform and wavelet-based feature extraction. Our study demonstrated significant advancements in surface electromyography signal classification, particularly in the Ninapro database 1 and CapgMyo dataset A, surpassing existing results in the literature. The newly introduced Signal Transformer outperformed traditional Convolutional Neural Networks by excelling in capturing structural details and incorporating global information from image-like signals through robust basis functions. Additionally, the inclusion of an attention mechanism within the Signal Transformer highlighted the significance of electrode readings, improving classification accuracy. These findings underscore the potential of the Signal Transformer as a powerful tool for precise and effective surface electromyography signal classification, promising applications in prosthetic control and rehabilitation.

Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.

A Novel Instruction Gesture Set Determination Scheme for Robust Myoelectric Control Applications.

Myoelectric control technology has important application value in rehabilitation medicine, prosthesis control, human-computer interaction (HCI) and other fields. However, the user dependence of electromyography (EMG) pattern recognition is one of the key problems hindering the implementation of robust myoelectric control applications. Aimed at solving the user dependence problem, this paper proposed a novel instruction gesture set determination scheme for EMG pattern recognition in user-independent mode. The scheme uses T-distributed stochastic neighbor embedding (T-SNE) dimensionality reduction to analyze high-dimensional surface EMG data from multiple users and gestures. This process can identify gesture combinations with minimal individual differences and high separability. The proposed scheme was validated using two large-scale EMG gesture databases with different acquisition devices, subjects, and gestures. Optimal and inferior gesture sets of varying sizes were identified. In recognition experiments conducted in both user-independent and electrode-offset modes, the optimal gesture sets demonstrated significantly higher recognition accuracies compared to the inferior sets, with improvements ranging from 12.57% to 36.92%. The results demonstrated that the separability of the obtained optimal gesture sets was significantly superior to that of the inferior sets, confirming the effectiveness of the proposed scheme in reducing user dependence in EMG pattern recognition. The study has certain application value to promote the development of myoelectric control technology. Specifically, the scheme proposed can be used to determine instruction gesture sets with low user dependence and high separability for myoelectric control applications.

Electromyography based hand movement classification and feature extraction using machine learning algorithms

The categorization of hand gestures holds significant importance in controlling orthotic and prosthetic devices, enabling human-machine interaction, and facilitating telerehabilitation applications. For many years, methods of motion analysis based on image processing techniques have been employed to detect hand motions. However, recent research has focused on utilizing muscle contraction for detecting hand movements. Specifically, there has been an increase in studies that classify hand movements using surface electromyography (sEMG) data from the muscles of the hand and arm. In our study, we estimated the open (extension of the fingers) and closed (flexion of the fingers) positions of the hand by analyzing EMG data obtained from 4 volunteer participants' Extensor digitorum and Flexor carpi radialis muscles. In order to accurately discriminate EMG signals, various statistical measures such as variance, standard deviation, root mean square, average energy, minimum and maximum features were utilized. The dataset containing these additional features was then subjected to classification algorithms including Support Vector Machines (SVM), K Nearest Neighbour (KNN), Decision Tree (DT), and Gaussian Naive Bayes (GNB) for the purpose of classifying hand positions into open or closed states. Among the tested algorithms, SVM achieved the highest success rate with a maximum accuracy of 73.1%, while KNN yielded the lowest success rate at a minimum accuracy of 55.9%. To further enhance prediction accuracy in future studies, it is suggested that data from a larger set of muscles be collected.

Lower limb muscle activity during first and second tennis serves: a comparison of three surface electromyography normalisation methods

ABSTRACT We assessed lower limb muscle activity during the execution of first and second tennis serves, exploring whether the extent of these differences is influenced by the chosen method for normalising surface electromyography (EMG) data. Ten male competitive tennis players first completed three rounds of maximal isometric voluntary contractions (MVC) of knee extensors and plantar flexors for the left (front) and right (back) leg separately, and three squat jumps. Afterward, they executed ten first and ten-second serves. Surface EMG activity of four lower limb muscles (vastus lateralis, rectus femoris, gastrocnemius lateralis, and soleus muscles) on each leg was recorded and normalised in three different ways: to MVC; to peak/maximal activity measured during squat jump; and to the actual serve. For the rectus femoris and soleus muscles of the left leg, and the gastrocnemius lateralis and soleus muscles of the right leg, EMG amplitude differed significantly between normalisation techniques (P ≤ 0.012). All muscles showed greater activity during the first serve, although this difference was only statistically significant for the right vastus lateralis muscle (P = 0.014). In conclusion, the EMG normalisation method selected may offer similar information when comparing first and second serve, at least for leg muscles studied here.

Blood flow restriction training improves the efficacy of routine intervention in patients with chronic ankle instability

As a new means of rehabilitation, blood flow restriction training (BFRT) is widely used in the field of musculoskeletal rehabilitation. To observe whether BFRT can improve the efficacy of routine rehabilitation intervention in patients with chronic ankle instability (CAI). Twenty-three patients with CAI were randomly divided into a routine rehabilitation group (RR Group) and a routine rehabilitation ​+ ​blood flow restriction training group (RR ​+ ​BFRT Group) according to the Cumberland Ankle Instability Tool (CAIT) score. The RR Group was treated with routine rehabilitation means for intervention, and the RR ​+ ​BFRT Group was treated with a tourniquet to restrict lower limb blood flow for rehabilitation training based on routine training. Before and after the intervention, the CAIT score on the affected side, standing time on one leg with eyes closed, comprehensive scores of the Y-balance test, and surface electromyography data of tibialis anterior (TA) and peroneus longus (PL) were collected to evaluate the recovery of the subjects. Patients were followed up 1 year after the intervention. After 4 weeks of intervention, the RR ​+ ​BFRT Group CAIT score was significantly higher than the RR Group (19.33 VS 16.73, p ​< ​0.05), the time of standing on one leg with eyes closed and the comprehensive score of Y-balance were improved, but there was no statistical difference between groups (p ​> ​0.05). RR ​+ ​BFRT Group increased the muscle activation of the TA with maximum exertion of the ankle dorsal extensor (p ​< ​0.05) and had no significant change in the muscle activation of the PL with maximum exertion of the ankle valgus (p ​> ​0.05). There was no significant difference in the incidence of resprains within 1 year between the groups (36.36% VS 16.67%, p ​> ​0.05). The incidence of ankle pain in the RR ​+ ​BFRT Group was lower than that in the RR Group (63.64% VS 9.09%, p ​< ​0.01). Therefore, four-weeks BFRT improves the effect of the routine intervention, and BFRT-related interventions are recommended for CAI patients with severe ankle muscle mass impairment or severe pain.

An electromyographical comparison of torso muscle activity and ratio during modified side bridge exercises.

Individualized exercise programs based on personal impairment could lead to successful rehabilitation. An effective way to train spine stability is to find exercises that take advantage of the synergistic relation between local and global stabilization systems. This study aimed to investigate synergistic relationship between the muscles of the local and global systems during three modified side bridge exercises compared with traditional side bridge (TSB). Twenty healthy participants performed TSB, both leg lift while side-lying (BLLS), torso lift on a 45∘ bench while side-lying (TLBS), and pelvic lift on side-lying (PLS) in random order. Surface electromyography data were analyzed. The results indicate that PLS was effective as TSB on trunk muscle activity. However, BLLS and TLBS demonstrated significantly less rectus abdominal (RA) muscle activity compared to TSB (p< .001). Additionally, BLLS and TLBS had a higher internal oblique (IO)/RA muscle activity ratio than TSB (p< .001). PLS could be a suitable alternative exercise for individuals who are unable to perform TSB, as it can effectively activate trunk muscles. BLLS and TLBS may be appropriate for training the local stability system, while limiting activation of the RA.

Kinematics, Kinetics and Muscle Activity Analysis during Single-leg Drop-jump Landing Followed by an Unanticipated Task: Focusing on Differences in Neurocognitive Function.

Lower neurocognitive function is a risk factor for anterior cruciate ligament (ACL) injury. However, the mechanism by which lower neurocognitive function increases the risk of ACL injury remains unclear. To clarify the effect of differences in neurocognitive function on landing mechanics during a single-leg drop-jump landing motion followed by an unanticipated task. Cross-sectional study. Fifteen collegiate female athletes were recruited (20.1 ± 1.3 years, 166.6 ± 7.3 cm, 60.6 ± 6.9 kg) and were divided into two groups (the high-performance (HP) group and the lower-performance (LP) group) using the median Symbol Digit Modalities Test (SDMT) score. Three-dimensional motion analysis was employed for the analysis during the experimental task of a single-leg drop-jump followed by an unanticipated landing task from a 30-cm high box. Joint angular changes of the trunk, pelvis, hip, and knee were calculated within the interval from initial contact (IC) to 40ms. Knee and hip moments were calculated as the maximum values within the interval from IC to 40ms. Surface electromyography data from key muscles were analyzed 50ms before and after IC. Independent t-tests were used to compare the effects of different neurocognitive function on the measurement items. Statistical significance was set at p < 0.05. The SDMT score was significantly higher in HP group (HP: 77.9 ± 5.5; LP: 66.0 ± 3.4; p < 0.001). The LP group had a significantly greater trunk rotation angular change to the stance leg side (HP: 0.4 ± 0.8; LP: 1.2 ± 0.4; p = 0.020). There were no significant differences between the two groups in terms of joint moments, and muscle activities. Differences in neurocognitive function by SDMT were found to be related to differences in motor strategies of the trunk in the horizontal plane. Although trunk motion in the sagittal and frontal planes during single-leg drop-jump landing increases the ACL injury risk by affecting knee joint motion, the effect of trunk motion in the horizontal plane remains unclear. 3© The Authors.

Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

Human-machine interfaces frequently use electromyography (EMG) signals. Based on previous work, feature extraction has a great deal of influence on the performance of EMG pattern recognition. Furthermore, the Deep Learning method is supposed to increase performance and not depend on feature engineering. However, directly processing raw signals will require a higher computation rate. This study proposed a new method that combines feature extraction and Deep Learning to address those issues while improving performance, reducing architecture size, and producing a more representative output. The proposed architecture employs the Transformer model as the backbone to get the correlation between elements and focus on the important information for estimating the flexion-extension of finger joint angles. This study uses experiment three of the NinaPro (Non-Invasive Adaptive Hand Prosthetics) DB5 dataset. Each experiment produces 16 Surface EMG data streams from two Myo Armbands devices representing 22 finger joint angles as output. This study compares the windowing process, feature extraction, execution time, and results with previous studies. The results show that the proposed model outperforms the previous study, from 0.957 in the previous study to become 0.970 in this study for the R-Square score. This result is obtained using 100 data points for the windowing process and Median Frequency for the best feature extraction method.

Muscle Activation and Ground Reaction Force between Single-Leg Drop Landing and Jump Landing among Young Females during Weight-Acceptance Phase.

Single-leg drop landing (SLDL) and jump landing (SLJL) are frequently used as assessment tools for identifying potential high-risk movement patterns; thus, understanding differences in neuromuscular responses between these types of landings is essential. This study aimed to compare lower extremity neuromuscular responses between the SLDL and SLJL. Thirteen female participants performed an SLDL and SLJL from a 30-cm box height. Vertical ground reaction force (vGRF), time to peak vGRF, and surface electromyography (sEMG) data were collected. Continuous neuromuscular responses, peak vGRF, and time to peak vGRF were compared between the tasks. Statistical parametric mapping (SPM) analysis demonstrated that the SLJL had a significantly higher sEMG activity in the rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM) within the first 10% of the landing phase compared with SLDL. At 20-30% of the landing phase, sEMGs in the RF and VL during the SLDL were significantly higher compared with SLJL (p < 0.05). A higher peak vGRF and shorter time to peak vGRF was observed during SLJL (p < 0.05). In conclusion, our findings highlight that SLJL exhibited greater RF, VL, and VM activities than SLDL at initial impact (10% landing), coinciding with a higher peak vGRF and shorter time to attain peak vGRF. Our findings support the role of the quadriceps as the primary energy dissipator during the SLJL.

The effects of trunk kinematics and EMG activity of wheelchair racing T54 athletes on wheelchair propulsion speeds.

The purpose of this study is to examine the impact of trunk kinematic characteristics and trunk muscle electromyography (EMG) activity on propulsion speeds in wheelchair racing T54 athletes. The Vicon infrared high-speed 3D motion capture system was utilized to acquire kinematic data of the shoulders, elbows, wrists, and trunk from twelve T54 athletes at four different speeds (5.55 m/s, 6.94 m/s, 8.33 m/s, and personal maximum speed). Additionally, the Trigno Wireless EMG system was employed to collect synchronous surface electromyography (EMG) data from the rectus abdominis and erector spinae muscles. The kinematics and EMG data of the trunk were compared across various wheelchair propulsion speeds while also examining the correlation coefficient between wheelchair propulsion speeds and: (1) the range of motion of upper limb joints as well as the trunk; (2) the maximum angular velocities of the upper limbs joints as well as the trunk; and (3) rectus abdominis and erector spinae EMG activity. Two multiple linear stepwise regression models were utilized to examine the impact of variables that had been identified as significant through correlation coefficient tests (1) and (2) on propulsion speed, respectively. There were significant differences in the range of motion (p<0.01) and angular velocity (p<0.01) of the athlete's trunk between different propulsion speeds. The range of motion (p<0.01, r=0.725) and angular speed (p<0.01, r=0.882) of the trunk showed a stronger correlation with propulsion speed than did upper limb joint movements. The multiple linear stepwise regression model revealed that the standardized β values of trunk motion range and angular velocity in athletes were greater than those of other independent variables in both models. In terms of the EMG variables, four of six variables from the rectus abdominis showed differences at different speeds (p<0.01), one of six variables from the erector spinae showed differences at different speeds (p<0.01). All six variables derived from the rectus abdominis exhibited a significant correlation with propulsion speed (p<0.05, r>0.3), while one variable derived from the erector spinae was found to be significantly correlated with propulsion speed (p<0.01, r=0.551). The movement of the trunk plays a pivotal role in determining the propulsion speed of wheelchair racing T54 athletes. Athletes are advised to utilize trunk movements to enhance their wheelchair's propulsion speed while also being mindful of the potential negative impact on sports performance resulting from excessive trunk elevation. The findings of this study indicate that it would be beneficial for wheelchair racing T54 athletes to incorporate trunk strength training into their overall strength training regimen, with a specific emphasis on enhancing the flexion and extension muscles of the trunk.

High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study

Objective. Studying motor units is essential for understanding motor control, the detection of neuromuscular disorders and the control of human–machine interfaces. Individual motor unit firings are currently identified in vivo by decomposing electromyographic (EMG) signals. Due to our body’s properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored. Approach. In this work, we perform in silico trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy. Main results. It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units. Significance. The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.

Trapezius activity during personal computer work with progressive addition lenses for general purpose and for computer work in neophytes.

This study analysed the impact of general purpose progressive addition lenses (GP-PALs) and computer progressive addition lenses (PC-PALs) on the activity of the trapezius muscle during computer operation. In this randomised, single-blinded, crossover study, surface electromyography (SEMG) signals were recorded bilaterally from the trapezius muscle during a 30-min computer task performed wearing different presbyopic corrections. The amplitude probability distribution function and its percentiles, gap frequency, muscular rest time and sustained low-level muscle activity periods were analysed in 32 subjects with artificially induced presbyopia. Subjectively perceived differences in vision and postural load between lenses were evaluated using a seven-item questionnaire (non-standardised, visual analogue scale ranged from 1 [bad] to 100 [good]). Considering the SEMG data, no significant difference in the muscular activity of the trapezius muscle was observed when using GP-PALs or PC-PALs for computer operation. However, PC-PALs showed statistically and clinically significantly higher results for subjectively perceived visual quality (78.4-31.3; p < 0.001), spontaneous tolerance (79.2-31.3; p < 0.001) and field of view (75.9-23.5; p < 0.001) compared with GP-PALs. Even though the electromyographic approach did not show a significant differentiation between the lenses, the subjective evaluation was clearly in favour of PC-PALs. Eye care practitioners should always take an occupational history of presbyopes, ask about the workplace situation and consider the use of PC-PALs.

Reliability in performance assessment creates a potential application of artificial intelligence in veterinary education: evaluation of suturing skills at a single institution.

To evaluate suturing skills of veterinary students using 3 common performance assessments (PAs) and to compare findings to data obtained by an electromyographic armband. 16 second-year veterinary students. Students performed 4 suturing tasks on synthetic tissue models 1, 3, and 5 weeks after a surgical skills course. Digital videos were scored by 4 expert surgeons using 3 PAs (an Objective Structured Clinical Examination [OSCE]- style surgical binary checklist, an Objective Structured Assessment of Technical Skill [OSATS] checklist, and a surgical Global Rating Scale [GRS]). Surface electromyography (sEMG) data collected from the dominant forearm were input to machine learning algorithms. Performance assessment scores were compared between experts and correlated to task completion times and sEMG data. Inter-rater reliability was calculated using the intraclass correlation coefficient (ICC). Inter-rater agreement was calculated using percent agreement with varying levels of tolerance. Reliability was moderate for the OSCE and OSATS checklists and poor for the GRS. Agreement was achieved for the checklists when moderate tolerance was applied but remained poor for the GRS. sEMG signals did not correlate well with checklist scores or task times, but features extracted from signals permitted task differentiation by routine statistical comparison and correct task classification using machine learning algorithms. Reliability and agreement of an OSCE-style checklist, OSATS checklist, and surgical GRS assessment were insufficient to characterize suturing skills of veterinary students. To avoid subjectivity associated with PA by raters, further study of kinematics and EMG data is warranted in the surgical skills evaluation of veterinary students.

Electromyographic analysis of trunk and hip muscles during Yoga poses prescribed for treating chronic low back pain

Yoga is effective for the management of chronic low back pain as it improves muscle strength and endurance. The objective of the current study was to assess trunk and hip muscle activation during Yoga poses usually prescribed for patients with chronic LBP. The study included 22 healthy Yoga trained subjects (mean age: 24.4 ± 2.6 years; 16 females, 6 males). The testing involved collecting surface electromyography data from Rectus Abdominis (RA) and Transverse Abdominis (TA), Gluteus Medius (GM), and Erector Spinae (ES) as subjects attained and held 16 different Yoga poses in standing, kneeling, supine, or prone positions in random order. The signal of each muscle was processed and normalized to its maximum voluntary isometric contraction (MVC). Statistical comparisons were made across selected poses and phases (attaining and holding) for each muscle using repeated-measures ANOVA. The data was also descriptively analyzed for sorting muscle activity. The activation of trunk flexors was significantly higher during boat pose (>50% MVC) followed by plank pose (∼30% MVC), activation of ES was significantly higher during reverse boat (41.7% ± 3.3 MVC) as compared to bow, snake, backward-sway, and warrior poses. The GM activation was significantly less in standing poses than during side-lying and Tiger poses (32–42% MVC). The cat-camel, kneeling camel, downward dog, backward-sway, swaying-palm tree, and warrior poses activated all tested muscles fairly (<20% MVC). The study helps the grading of Yoga positions according to the challenge imposed. The challenging poses may be used to develop graded rehabilitation programs to improve muscle strength/endurance.

Acute Neuromuscular Fatigability and Perceived Exertion in High-Level University Pianists: An Electromyography and Maximum Force Capacity Study.

Musicians are labelled 'fine motor athletes' due to the significant demands of practice and performance. In response, sports medical concepts and expertise have been adapted and mobilized to address musicians' health considerations. However, understanding distinct differences between the sports and performing arts medical contexts is critical. Mismanagement of fatigue and acute fatigability are identified as key risk factors for deleterious symptoms and injuries in musicians. Acute fatigability in athletes and musicians have been assumed to occur similarly, although this has yet to be empirically demonstrated. This study aimed to evaluate acute muscular performance fatigability during piano performance of varied intensities. Fourteen university pianists attended a single experimental session and performed three pieces of randomly ordered repertoire for 10 minutes each. Repertoire were self-selected to be physically easy (rating of perceived exertion [RPE] 9/20), somewhat hard (RPE 13/20), or hard (RPE 17/20). At baseline, after each repertoire performance, and following a concluding 10-minute silent sitting period, participants completed maximum voluntary contraction (MVC) grip and shoulder strength tests. Surface electromyographic (EMG) data were obtained from bilateral upper trapezius and forearm flexor and extensor muscles. Data were analyzed using uni- and multivariate repeated measures analyses of variance (ANOVAs). No evidence of muscular performance fatigability was found in study pianists at any intensity. Mean EMG amplitude and average median EMG frequency did not significantly vary during any repertoire performances or MVC tests (F≤1.72; p>0.09). Additionally, no changes in force or EMG parameters were found during any MVC tests (F≤1.19; p>0.31). The absence of muscular performance fatigability during even the most intense piano performances suggests substantially differing mechanisms of fatigability and perceived exertion in pianists versus those commonly seen in sport athletes. Further research is needed to understand the mechanisms of acute fatigability in musicians and develop musician-specific management strategies.

Electromyographic and Kinematic Comparison of the Leading and Trailing Fore- and Hindlimbs of Horses during Canter.

This study compared muscle activity and movement between the leading (Ld) and trailing (Tr) fore- (F) and hindlimbs (H) of horses cantering overground. Three-dimensional kinematic and surface electromyography (sEMG) data were collected from right triceps brachii, biceps femoris, middle gluteal, and splenius from 10 ridden horses during straight left- and right-lead canter. Statistical parametric mapping evaluated between-limb (LdF vs. TrF, LdH vs. TrH) differences in time- and amplitude-normalized sEMG and joint angle-time waveforms over the stride. Linear mixed models evaluated between-limb differences in discrete sEMG activation timings, average rectified values (ARV), and spatio-temporal kinematics. Significantly greater gluteal ARV and activity duration facilitated greater limb retraction, hip extension, and stifle flexion (p < 0.05) in the TrH during stance. Earlier splenius activation during the LdF movement cycle (p < 0.05), reflected bilateral activation during TrF/LdH diagonal stance, contributing to body pitching mechanisms in canter. Limb muscles were generally quiescent during swing, where significantly greater LdF/H protraction was observed through greater elbow and hip flexion (p < 0.05), respectively. Alterations in muscle activation facilitate different timing and movement cycles of the leading and trailing limbs, which justifies equal training on both canter leads to develop symmetry in muscular strength, enhance athletic performance, and mitigate overuse injury risks.

The effects of transcutaneous electrical nerve stimulation on muscle activity pattern in professional voice users with muscle tension dysphonia using surface electromyography data: A pilot study

ObjectivesMuscle Tension Dysphonia is a voice disorder, which results in stiffness in the laryngeal extrinsic muscles, intense collision, painful contractions, and vibrations of the vocal cords. Due to the multifactorial identity of Muscle Tension Dysphonia, its therapeutic approach must be multidisciplinary. MethodsThe participants were divided into two groups: a control group (5participants) that received Circumlaryngeal Manual Therapy (CMT) + Placebo Transcutaneous Electrical Nerve Stimulation and an experimental group (5participants) that received Transcutaneous Electrical Nerve Stimulation + CMT. Both groups received 10 sessions of treatment, twice a week, for 40 min each. Before and after treatment, participants were assessed using the Dysphonia Severity Index (DSI) and surface electromyography for their ability to sustain the vowels /e& u/and count from 20 to30. ResultAfter therapy, there were substantial improvements in the DSI (2.72 ± 0.55, P < 0.05) and muscle electrical activity in the control group. The DSI (3.66 ± 0.63, P < 0.05) and muscle electrical activity were also significantly improved in the experimental group after treatment. The findings of the between-group comparison after treatment revealed a significantly greater increase in the Dysphonia Severity Index in the experimental group compared with the control group (P = 0.037). Although there was no significant difference between the two groups in terms of muscle electrical activity, clinically significant changes were more noticeable in the experimental group when compared with the control group. ConclusionsPositive results were seen in both groups. The results demonstrate that both approaches relax vocal tract muscles. As a result, Transcutaneous Electrical Nerve Stimulation was recommended as a complementary treatment for clients with Muscle Tension Dysphonia.

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue.

Characterization of fatigue using surface electromyography (sEMG) data has been motivated for rehabilitation and injury-preventative technologies. Current sEMG-based models of fatigue are limited due to (a) linear and parametric assumptions, (b) lack of a holistic neurophysiological view, and (c) complex and heterogeneous responses. This paper proposes and validates a data-driven non-parametric functional muscle network analysis to reliably characterize fatigue-related changes in synergistic muscle coordination and distribution of neural drive at the peripheral level. The proposed approach was tested on data collected in this study from the lower extremities of 26 asymptomatic volunteers (13 subjects were assigned to the fatigue intervention group, and 13 age/gender-matched subjects were assigned to the control group). Volitional fatigue was induced in the intervention group by moderate-intensity unilateral leg press exercises. The proposed non-parametric functional muscle network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. For the first time, this paper proposed a non-parametric functional muscle network and highlighted the corresponding potential as a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

SDAV 1.0: A Low-Cost sEMG Data Acquisition &amp; Processing System For Rehabilitatio

Over the last two decades, myoelectric signals have been widely used in fields including rehabilitation devices and human-machine interfaces. This study aimed to develop an algorithm for surface electromyography (sEMG) data acquisition utilizing low-cost hardware and validate its performance using English vowels as silent speech content. The sEMG data were collected from the three facial muscles of one healthy subject. The sEMG signals were pre-processed, and various time-domain and statistical features were extracted in real time. The raw data and features were then used to train and test three customized machine learning classifiers: k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN). All customized classifiers achieved almost equivalent accuracy rates of 0.83 ± 0.01 in recognizing the English vowels with an improvement of 27.27% (KNN), 3.75% (SVM), and 51.85% (ANN) utilizing the same low-cost data acquisition hardware. Our findings are substantially closers to the results of commercial hardware setups, which raise the possibility of potential usage of low-cost sEMG data acquisition systems with the proposed algorithm in place of commercial hardware setups for rehabilitation devices and other related sectors of human-machine interaction.