Surface Electromyography Data Research Articles

Electromyography signal based hand gesture classification system using Hilbert Huang transform and deep neural networks

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives).

Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition.

Modular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative Tucker decomposition (NTD) method. Surface electromyography (sEMG) data of 8 upper limb muscles in 10 healthy subjects under wrist flexion (WF) and wrist extension (WE) were recorded. NTD was selected for exploring the multi-domain muscle synergy from the sEMG data. The results showed two synergistic flexor pairs, Palmaris longus-Flexor Digitorum Superficialis (PL-FDS) and Extensor Carpi Radialis-Flexor Carpi Radialis (ECR-FCR), in the WF stage. Their spectral components are mainly in the respective bands 0-20 Hz and 25-50 Hz. And the spectral components of two extensor pairs, Extensor Digitorum-Extensor Carpi Ulnar (ED-ECU) and Extensor Carpi Radialis-Brachioradialis (ECR-B), are mainly in the respective bands 0-20 Hz and 7-45 Hz in the WE stage. Additionally, further analysis showed that the Biceps Brachii (BB) muscle was a shared muscle synergy module of the WE and WF stage, while the flexor muscles FCR, PL and FDS were the specific synergy modules of the WF stage, and the extensor muscles ED, ECU, ECR and B were the specific synergy modules of the WE stage. This study showed that NTD is a meaningful method to explore the multi-domain synergistic characteristics of multi-channel sEMG signals. The results can help us to better understand the frequency features of muscle synergy and shared and specific synergies, and expand the study perspective related to motor control in the nervous system.

Sustained Muscle EMG Activity to Contractile Failure During Incremental Exercise and Intense Constant Load Cycling: No Evidence of a Central Governor

Since 1997, debate has continued over the presence of a central governor that constrains neuromuscular activity during severe, intense exercise. This study aimed to challenge the central governor model (CGM) through acquiring surface electromyography (sEMG) data from the vastus lateralis (VL) and gluteus maximus (Gmax) muscles of 14 healthy participants during 4 different bouts of constant load, non-steady state cycling exercise (110, 125, 140, 160 %watts at the ventilation threshold), and 1 incremental bout to volitional exhaustion. sEMG activity was processed to isolate and capture each contraction of the VL and Gmax during all bouts of exercise. sEMG data was then graphed to profile sEMG root mean square (rms) activity over time with linear curve fitting used to quantify this relationship for data preceding (segment 1) and during the final 30s of each test (segment 2). Two-way repeated measures ANOVA was used to test for differences between the slopes of the two linear segments of the sEMG rms response of the VL for each bout. Results during the VO2max trial revealed a significant main effect for SEGMENT where segment 2 was significantly greater than segment 1 (F=6.741, p=0.023). During the critical power trails there were significant differences in sEMG rms for each of INTENSITY (F=9.349, p<0.001), SEGMENT (F=5.443, p=0.036), and the interaction effect (F=2.837, p=0.005). Muscle sEMG rms data revealed sustained increases in muscle activity in all bouts of intense exercise to volitional exhaustion in both the VL and Gmax, which is inconsistent with the predictions made from the CGM.

Passive activity enhances residual control ability in patients with complete spinal cord injury

Abstract Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury level. However, because of prolonged inactivity, initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway. A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation, as measured by surface electromyography. In this study, we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord injury. Eleven patients with chronic complete thoracic spinal cord injury were recruited. Surface electromyography data from eight major leg muscles were acquired and compared before and after the passive activity protocol. The results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of activation. Although the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity, the difference was not statistically significant. These findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.

Comparison of Two Types of Electrodes for Measuring Submental Muscle Activity During Swallowing.

This study aimed to investigate the potential of a newly developed small electrode to accurately record muscle activity during swallowing. This study included 31 healthy participants. The participants underwent swallowing trials with three types of material. The recordings involved the following conditions: 1) swallowing saliva, 2) swallowing 3 mL water, and 3) swallowing 5 mL water. Two types of electrodes, a conventional electrode (CE) and a newly developed small electrode (NE), were symmetrically positioned on the skin over the suprahyoid muscle group, starting from the center. From the surface electromyography data, the swallowing duration (s), peak amplitude, and rising time (duration from swallowing onset to peak amplitude: s) were measured. Additionally, the equivalence of characteristics of the waveform of muscle activities was calculated by using the variance in both the upper and lower confidence limits in duration and rising time. No significant differences in baseline, swallowing duration or rising time between the CE and NE were observed for any swallowing material. The peak amplitude was significantly higher for the NE than for the CE for all swallowing materials. The CE and NE displayed no significant difference in the equivalence of characteristics of the waveform of muscle activities for any swallowing material. The gold-plated small electrodes utilized in this study indicated the ability to record the same characteristics of muscle activity as conventional electrodes. Moreover, it was able to capture the muscle activity of each muscle group with improved sensitivity in a narrow area, such as under the submandibular region, with more precision than that of conventional electrodes.

In-Laboratory Polysomnography Worsens Obstructive Sleep Apnea by Changing Body Position Compared to Home Testing.

(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea-hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA.

Effects of Ankle Position While Performing One- and Two-Leg Floor Bridging Exercises on Core and Lower Extremity Muscle Recruitment.

Given there are no known studies which have examined multiple lower extremity muscles between different ankle positions during bridging activities, the objective was to assess how employing two different ankle positions (PF versus DF) while performing five common bridging exercises (three bipedal and two unipedal) used in rehabilitation and athletic performance affect core and select lower extremity muscle EMG recruitment. Twenty healthy subjects performed a 5 s isometric hold during five two- and one-leg bridge exercises: (1) on right leg with left knee to chest (1LB-LFlex); (2) on right leg with left knee extended (1LB-LExt); (3) standard two-leg bridge (2LB); (4) two-leg bridge with resistance band around knees (2LB-ABD); and (5) two-leg bridge with ball between knees (2LB-ADD). Surface electromyographic (EMG) data were collected using a Noraxon Telemyo Direct Transmission System from fourteen muscles: (1) three superficial quadriceps (VM, VL, and RF); (2) three hip abductors (TFL, GMED, and GMAX); (3) medial hamstrings (ST) and lateral hamstrings (BF); (4) hip adductors (ADD); (5) erector spinae (ES); (6) latissimus dorsi (LATS); (7) upper rectus abdominis (RA); and (8) external oblique (EO) and internal oblique (IO). EMG data were normalized by maximum voluntary isometric contractions (MVICs). A paired t-test (p < 0.01) was used to assess differences in normalized mean EMG activities between DF and PF for each exercise. EMG activities were significantly greater in DF than PF for the (a) VM, VL, and RF during 1LB-LFlex; (b) ADD during 1LB-LFlex, 1LB-LExt; (c) EO during 1LB-LFlex; and (d) IO during 1LB-LFex. In contrast, EMG activities were significantly greater in PF than DF for ST and BF during all five bridge exercises. Bridging with PF (feet flat) was most effective in recruiting the hamstrings, while bridging with DF (feet up) was most effective in recruiting the quadriceps, hip adductors, and internal and external obliques.

Towards standardisation of surface electromyography measurements in the horse: Bipolar electrode location

The use of surface electromyography in the field of animal locomotion has increased considerably over the past decade. However, no consensus exists on the methodology for data collection in horses. This study aimed to start the development of recommendations for bipolar electrode locations to collect surface electromyographic data from horses during dynamic tasks.Data were collected from 21 superficial muscles of three horses during trot on a treadmill using linear electrode arrays. The data were assessed both quantitatively (signal-to-noise ratio (SNR) and coefficient of variation (CoV)) and qualitatively (presence of crosstalk and activation patterns) to compare and select electrode locations for each muscle.For most muscles and horses, the highest SNR values were detected near or cranial/proximal to the central region of the muscle. Concerning the CoV, there were larger differences between muscles and horses than within muscles. Qualitatively, crosstalk was suspected to be present in the signals of twelve muscles but not in all locations in the arrays.With this study, a first attempt is made to develop recommendations for bipolar electrode locations for muscle activity measurements during dynamic contractions in horses. The results may help to improve the reliability and reproducibility of study results in equine biomechanics.

Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing.

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.

Comparing face-tracking action units with electromyography during speech

Video face-tracking software, such as OpenFace 2.0, can be used to make inferences about facial muscle activation [Baltrušaitis et al. IEEE 13, 59–66 (2018)]. However, the accuracy of these inferences based on the facial action units (FAUs) calculated by OpenFace 2.0 compared to corresponding muscle activity during speech is unclear. A previous study investigated muscle activation when both smile and speech occur simultaneously, focusing on the zygomaticus major (ZM) muscle [Liu et al. ISSP, 130–133 (2021)], but only presented data for a single speaker and did not compare FAU and EMG results. The present study compares OpenFace 2.0 action units with surface electromyography (EMG) data during speech in order to assess the validity of these inferences about facial muscle activation. We compare ZM activity and the lip corner puller FAU intensity results from a dataset collected for the previously mentioned [Liu et al. 2021] study. Data include four speakers producing read speech in smile conditions; 2 s will be extracted from EMG and FAU data before and after each utterance. Results will be reported on the relative accuracy of the FAU and EMG data. Implications will be discussed for speech communication research. [Work supported by NSERC.]

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.

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.