Electromyography Sensors Research Articles

Relationship Between Muscle Activity and Force During Suspended Bridge Exercises.

Isometric core stability exercises are commonly used to target muscles of the lumbopelvic-hip complex, including the rectus abdominis (RA) and erector spinae (ES). These exercises can be implemented in rehabilitation protocols to increase muscle strength and endurance. Difficulty can be progressed by modifying the base of support or adding an unstable element. Load cells can be affixed to suspension training devices to measure force exerted through the straps during exercise. The primary purpose of this study was to assess the relationship between activity of the RA and ES to force measured through a load cell fixed to suspension straps during bilateral and unilateral suspended bridge exercises. Forty asymptomatic, active individuals completed a single laboratory visit. Participants completed 2 bilateral suspended bridges and 2 unilateral suspended bridges held to failure. Surface electromyography sensors were placed over the bilateral RA and ES to quantify muscle activity (% maximum voluntary isometric contraction). A load cell was affixed to the suspension straps to measure force exerted through the straps throughout the duration of the exercise. Pearson correlations were used to determine relationships between force and muscle activity of the RA and ES throughout exercise duration. Force was negatively related to RA muscle activity in bilateral suspended bridges (r = -.735 to -.842, P < .001) and unilateral suspended bridges (r = -.300 to -.707, P = .002 to <.001). Force had a positive relationship with ES muscle activity in bilateral suspended bridges (r = .689 to .791, P < .001) and unilateral suspended bridges (r = .418 to .448, P < .001). Suspended bridge exercises can be a valuable tool to target posterior abdominal musculature such as the ES to contribute to core stability and endurance. Load cells can be applied during suspension training to quantify the interaction between individuals and the exercise equipment.

Effect of unilateral ankle loading on gait symmetry in young adults

BackgroundWalking requires constant adjustments to the changing environment. An asymmetrical perturbation can affect the gait symmetry, cause gait adaptations, and potentially induce retention of the adapted gait after removal of the perturbation. A unilateral ankle load has the potential to create asymmetry and facilitate the emergence of new gait patterns. However, few studies have examined the effect of unilateral loading on muscular adjustments during walking. The purpose of this study was to investigate gait adaptations and muscular adjustments after unilaterally loading or unloading the ankle. Research questionWhat are the effects of unilateral loading and unloading on gait spatiotemporal parameters and muscle activation in young adults? MethodsTwenty young adults (10 M/10 F) walked on a treadmill at their preferred walking speeds in 3 conditions: 1) a 2-minute baseline trial; 2) three 5-minute trials with a load (3 % of bodyweight) on the dominant ankle (Loading); and 3) one 5-minute trial with the load removed (Unloading). Inertial measurement units (IMUs) and electromyography sensors (EMGs) were used for data collection. Early and late adaptation and post-adaptation were assessed using the first 5 strides and the last 30 strides of loading and unloading conditions. Outcome measures included symmetry index (SI) of spatiotemporal parameters, range-of-motion (ROM) of the lower body joints, and EMG integrals of leg muscles. Repeated measures ANOVA was conducted for statistical analysis (α = 0.05). ResultsSI of swing phase percentage demonstrated rapid adaptation after unilateral loading or unloading. Stride length demonstrated an aftereffect following unloading. Young adults reduced ankle ROMs bilaterally in early adaptation and increased loaded-side knee and hip ROMs in late adaptation. Additionally, they increased the tibialis anterior activity bilaterally immediately after unilateral loading. SignificanceYoung adults showed an aftereffect in some variables after unilateral unloading, signifying that unilateral ankle loading can induce short term learning of a new gait pattern.

Assessing Quadriceps Muscle Contraction Using a Novel Surface Mechanomyography Sensor during Two Neuromuscular Control Screening Tasks.

Electromyography (EMG) is the clinical standard for capturing muscle activation data to gain insight into neuromuscular control, yet challenges surrounding data analysis limit its use during dynamic tasks. Surface mechanomyography (sMMG) sensors are novel wearable devices that measure the physical output of muscle excursion during contraction, which may offer potential easy application to assess neuromuscular control. This study aimed to investigate sMMG detection of the timing patterns of muscle contraction compared to EMG. Fifteen healthy participants (mean age = 31.7 ± 9.1 y; eight males and seven females) were donned with EMG and sMMG sensors on their right quadriceps for simultaneous data capture during bilateral deep squats, and a subset performed three sets of repeated unilateral partial squats. No significant difference in the total duration of contraction was detected by EMG and sMMG during bilateral (p = 0.822) and partial (p = 0.246) squats. sMMG and EMG timing did not differ significantly for eccentric (p = 0.414) and concentric (p = 0.462) phases of muscle contraction during bilateral squats. The sMMG magnitude of quadriceps excursion demonstrated excellent intra-session retest reliability for bilateral (ICC3,1 = 0.962 mm) and partial (ICC3,1 = 0.936 mm, n = 10) squats. The sMMG sensors accurately and consistently provided key quadriceps muscle performance metrics during two physical activities commonly used to assess neuromuscular control for injury prevention, rehabilitation, and exercise training.

Evaluating Gait Stability and Muscle Activation in Different Hand Holding Conditions Using the Robotic Walker-mTPAD.

While walkers are used as mobility aids for different gait impairments, little is known about the factors that affect the performance of such aids. Therefore, we investigated the impact of arm-holding conditions on gait stability and muscle activation. We used surface electromyography (sEMG) sensors on specific arm and leg muscles while the users took laps with a robotic walker, the mobile Tethered Pelvic Assist Device (mTPAD), on an instrumented mat. Eleven participants without gait disorders walked with and without a 10% body weight (BW) force applied on the pelvis in the following three configurations: (i) while gripping the walker's frame, (ii) while using an armrest with their arms at a 90∘ angle, and (iii) while using an armrest with their arms at a 130∘ angle for 5 min each. Our results showed that when applying a force, the users changed their gait to increase stability. We also discovered differences in muscle activation based on the user's specific arm conditions. Specifically, the 130∘ condition required the least muscle activation, while gripping the walker's frame increased specific muscle activation compared to 90∘ and 130∘. This study is the first to evaluate how arm-holding and external loading conditions alter gait and muscle activations using the mTPAD.

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Gesture recognition, as a core technology of human–computer interaction, has broad application prospects and brings new technical possibilities for smart homes, medical care, sports training, and other fields. Compared with the traditional human–computer interaction models based on PC use with keyboards and mice, gesture recognition-based human–computer interaction modes can transmit information more naturally, flexibly, and intuitively, which has become a research hotspot in the field of human–computer interaction in recent years. This paper described the current status of gesture recognition technology, summarized the principles and development history of electromagnetic wave sensor recognition, stress sensor recognition, electromyographic sensor recognition, and visual sensor recognition, and summarized the improvement of this technology by researchers in recent years through the direction of sensor structure, selection of characteristic signals, the algorithm of signal processing, etc. By sorting out and comparing the typical cases of the four implementations, the advantages and disadvantages of each implementation and the application scenarios were discussed from the two aspects of dataset size and accuracy. Based on the abovementioned discussion, the problems and challenges of current gesture recognition technology were discussed in terms of the biocompatibility of sensor structures, wearability and adaptability, stability, robustness, and crossover of signal acquisition and analysis algorithms, and the future development directions in this field were proposed.

SEMG APPROACH FOR SPEECH RECOGNITION

Speech is the most familiar and habitual way of communication used by most of us. Due to speech disabilities, many people find it difficult to properly voice their views and thus are at a disadvantage. The research tackles the issue of lack of speech from a speech impaired user by recognizing it with the use of ML models such as Gaussian Mixture Model - GMM and Convolutional Neural Network - CNN. With properly recorded and cleaned muscle activity from the facial muscles it is possible to predict the words being uttered/whispered with a certain accuracy. The intended system will additionally also have a visual aid system which can provide better accuracy when used together with the facial muscle activity-based system. Neuromuscular signals from the speech articulating muscles are recorded using Surface Electro Myo Graphy (SEMG) sensors, which will be used to train the machine learning models. In this paper we have demonstrated various signals synthesized through the ElectroMyography system and how they can be classified using machine learning models such as Gaussian Mixture Model and Convolutional Neural Network for the visual-based lip-reading system.

Field measurement of hand forces of palm oil harvesters and evaluating the risk of work-related musculoskeletal disorders (WMSDs) through biomechanical analysis

Hand force data is critical in evaluating work-related musculoskeletal disorders (WMSDs). Nevertheless, earlier studies on oil palm workers relied on estimated or laboratory measurements, which may not accurately reflect the actual hand forces. This study is the first report on the field measurement of hand forces for palm oil harvesters using a chisel and sickle to harvest low and tall palm trees, respectively. The dynamic hand forces and ground reaction forces were measured using instrumented harvesting tools and force plates, while wearable motion (IMU) and electromyography (EMG) sensors were incorporated for quantifying postural angles and muscle activations, respectively. Additionally, the spinal loadings, continuous Rapid Entire Body Assessment (REBA) scores, and subjective pain scores were determined to evaluate the risk of WMSDs. A total of 10 harvesters were recruited to perform the palm pruning tasks using a chisel and sickle. Resultantly, the sickle and chisel recorded a maximum cutting force of 1601.23 ± 424.26 N and 420.80 ± 96.00 N, respectively. All pruning tasks were found to be highly risky to harvesters, with a peak REBA score of 12. Likewise, all investigated muscles were activated for over 40% MVC, thus inducing moderate pain in the muscles. The peak L5-S1 compression forces for all tasks exceeded the safety threshold (>3400 N), but the values were not significantly different. The shear force of the L5-S1 was extreme in pruning with a sickle (1446.10 ± 411.00 N) compared to using a chisel. In conclusion, palm harvesters were at a high risk of developing WMSDs following poor postures, high physical exertion and muscle activity, and excessive spinal loads.

Electromyographic and Stabilometric Analysis of the Static and Dynamic "Standing Bird Dog" Exercise.

(1) Background: The "bird dog" exercise is considered one of the most effective therapeutic exercises for lumbopelvic rehabilitation and the prevention and treatment of low back pain. The "standing bird dog" (SBD) exercise, executed in a single-leg stance, constitutes a natural and challenging variation in the "bird dog"; nevertheless, this exercise has not yet been investigated. This study provides a stabilometric and electromyographic analysis of the SBD performed in static and dynamic conditions and in ipsilateral and contralateral variations; (2) Methods: A time-synchronized motion capture system, wireless electromyography sensors, and triaxial force platform were used to analyze the selected SBD exercises; (3) Results: In dynamic conditions, the gluteus maximum, multifidus, lumbar erector spinae, and gluteus medius reached a mean activation level higher than in the static condition, with peak activation levels of 80%, 60%, 55%, and a 45% maximum voluntary isometric contraction, respectively. In the static condition, balance control was more challenging in the mediolateral compared to the anteroposterior direction. In the dynamic condition, the balance challenge was higher in the anteroposterior direction and higher than the static condition in both directions; (4) Conclusions: The SBD was proved to be effective for strengthening the hip and lumbar extensor muscles and provided a powerful challenge to single-leg balance control in both mediolateral and anteroposterior directions.

HUMAN GAIT ANALYSIS USING HYBRID CONVOLUTIONAL NEURAL NETWORKS

Human gait analysis is a promising method of researching on human activities like walking or sitting. It reflects the habits of one person and can be observed in any activity that person performs. The patterns in human movements are influenced by many factors, including physiology, social, psychological, and health factors. Differences in limb movements help identify gait patterns, which are often measured using inertial measurement unit sensors (IMU) like gyroscopes and accelerometers placed in various locations throughout the body. This paper analyses the combination of IMU sensors and electromyography sensors (EMG) to improve the identification accuracy of human movements. We propose the hybrid convolutional neural network (CNN) and long short-term memory neuron network (LSTM) for the human gait analysis problem to achieve an accuracy of 0.9418, better than other models including pure CNN models. By using CNN's image classification advancements, we analyse multivariate time series sensor signals by using a sliding window to transform sensor data into image representation and principal component analysis (PCA) to reduce the data dimensionality. To tackle the dataset imbalance issue, we re-weight our model loss by the inverse effective number of samples in each class. We use the human gait HuGaDB dataset with unique characteristics, for gait analysis.

Design of bionic prosthetic leg

Along with the increase in the number of Indonesian people who are physically disabled and the number of cases of leg amputations above the knee, the need for prosthetic limbs in Indonesia is also increasing. Prosthetic limbs are tools to replace lost limbs caused by birth defects, amputations, or disease. The bionic prosthetic leg has a long-term useful purpose, as well as supporting the rights of people with disabilities. The device helps the above-the-knee amputee. To design a bionic prosthetic leg, a biomechanical analysis of work is needed to obtain a walking pattern in humans and an analysis of the structure and working material to obtain a safe and comfortable material for use in a bionic prosthetic foot. This bionic leg prosthetic uses an automatic system using an Electromyography (EMG) sensor as a signal picker for the muscles, Arduino Mega 2560 as an input signal processor which is then converted into an output signal and Planetary gear as a knee joint transmission so that the body does not fall forward or backward. The results of the analysis using Autodesk Inventor software on the prosthetic of the bionic leg can withstand a pressure of 404.526 MPa, withstand a reaction force of 354.576 N and a reaction moment of 15.4073 N. So based on the results of the bionic prosthetic leg is very safe and comfortable when used on people with disabilities.

Muscle fatigue during assisted violin performance

Muscle fatigue is a primary risk factor in developing musculoskeletal disorders, which affect up to 93% musicians, especially violinists. Devices providing dynamic assistive support (DAS) to the violin-holding arm can lessen fatigue. The objective was to assess DAS effects on electromyography median frequency and joint kinematics during a fatiguing violin-playing task. Fifteen university-level and professional violinists were equipped with electromyography sensors and reflective markers to record upper-body muscle activity and kinematics. They played G scales with and without DAS until exhaustion. Paired t-tests assessed DAS effects on delta (final–initial) electromyography median frequencies and joint kinematics. DAS prevented the median frequency decrease of left supraspinatus, superior trapezius, and right medial deltoid, and increases in trunk rotation, left-wrist abduction, and right arm-elevation plane. DAS effects on kinematics were marginal due to retention of musical performance despite fatigue. However, DAS reduced fatigue of several muscles, which is promising for injury prevention. Practitioner summary: Violinists are greatly affected by musculoskeletal disorders. Effects of a mobility assistive device on muscle fatigue during violin playing was investigated. The assistive technology slowed down the development of fatigue for three neck/shoulder muscles, making assisted musical performance a promising avenue to prevent violinists’ injuries.

The biomechanical study of human limbs movement using inertial and electromyographic sensors

The biomechanical study of human limbs movement using inertial and electromyographic sensors

Classification of gait phases based on a machine learning approach using muscle synergy.

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.

Real-Time Intent Sensing for Assistive Devices with Implications for Minimising Maintenance

Intent sensing is a growing field within medical device control, with major potential applications for assistive devices, such as prosthetics. As many sensors as possible should be utilised to maximise accuracy. The availability of sensors may change over time due to changing surroundings or activities, sensors failing, and electrode contact being lost. The sensor network should be dynamic and modular in nature, continuing to function even when some sensors are unavailable. The management of sensor unavailability may help to reduce the need for device maintenance, particularly in developing nations with limited availability of these services. An algorithm is proposed to classify intent using networked sensors in real time. Data are gathered using human participants wearing four surface electromyography sensors and performing a pseudo-random sequence of grasps. The relationship between time offset and prediction accuracy is investigated, with the algorithm predicting future intent actions up to half a second in advance. Sensor dropout is simulated by randomly replacing sensor readings with recorded noise. The new algorithm is compared to existing algorithms and shown to be more accurate in situations of sensor dropout, with the difference increasing as more sensors become unavailable. This suggests that when reductions in sensing capabilities are likely to occur over time, the modular method is more appropriate for control.

Offset Loading in a Bilateral Squatting Movement Pattern Influences Ground-Reaction Force and Muscle Activity in the Dominant and Nondominant Limb.

The purpose of this study was to explore whether offset loading in the barbell squat altered ground-reaction force (GRF) and muscle activation in the dominant (D) and nondominant (ND) lower limb compared to traditional squats. Twelve well-trained men (age 26.4 [3.2]y; 10.3 [1.9]y experience) performed 3 sets of 10 repetitions at 60% of their previously measured 1-repetition maximum. Sets were quasi-randomized between traditional loading (TDL), dominant-side offset loading (OS-D), and nondominant-side offset loading (OS-ND). All repetitions were performed on a dual force plate with electromyography sensors on the prime mover muscles of the squat. GRF symmetry was assessed using the symmetry index (SI) to determine the direction (D [+] or ND [-]) and magnitude (%) of the asymmetry. Finally, the first 3 and final 3 repetitions of each set were compared for compensatory changes in symmetry. OS-D induced a significant change in limb SI relative to TDL (5.21% vs 1.44%; P = .011); however, no significant difference in limb SI was seen between TDL and OS-ND (-0.66% vs 1.44%; P = .278). No asymmetries between D and ND muscle activation were present in any condition. TDL and OS-D squats exhibited significant improvements in limb SI between the first 3 and final 3 repetitions (P = .035 and .011, respectively); however, no such improvement was seen in OS-ND. OS-D is capable of significantly altering GRF limb SI in a bilateral squat; however, OS-ND appears to exhibit no GRF or electromyography effects relative to TDL. Thus, the results of this study do not support the use of OS-ND in the pursuit of strengthening a weaker limb, suggesting that unilateral training may be a preferred mode of exercise for this desired outcome.

Machine learning full 3-D lower-body kinematics and kinetics on patients with osteoarthritis from electromyography

Osteoarthritis (OA) is a degenerative disease that causes severe pain and reduces the range of motion of the joint, decreasing the quality of life for millions of individuals in the United States. Electromyography (EMG) sensors have been widely studied in biomechanics, showing applications in prosthetics, robotics, and control. While complex musculoskeletal models have been well established, the attempt of directly correlating EMG with kinematics and kinetics is still quite limited. Particularly, little work has been conducted on OA patients. In this work, we propose a method for estimating lower body joint angles (JAs) and ground reaction forces (GRFs) from surface-EMG sensors during a step-down task for individuals diagnosed with OA. The JAs and GRFs were measured by a Vicon motion capture system and force plates, respectively. EMG, JAs, and GRFs were used to train an echo state network (ESN) which afforded JAs with relative errors of 3.78% and 3.71% and the GRFs with relative errors of 3.619% and 4.596% for training and testing datasets, respectively. This study suggests the high fidelity of the ESN in automatically predicting full lower body kinematics and kinetics from the EMG signals. The results of this work promote the development of an EMG-controlled lower limb rehabilitation robot system for patients with OA.

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.

Evaluation of Novel Embroidered Textile-Electrodes Made from Hybrid Polyamide Conductive Threads for Surface EMG Sensing.

Recently, there has been an increase in the number of reports on textile-based dry electrodes that can detect biopotentials without the need for electrolytic gels. However, these textile electrodes have a higher electrode skin interface impedance due to the improper contact between the skin and the electrode, diminishing the reliability and repeatability of the sensor. To facilitate improved skin-electrode contact, the effects of load and holding contact pressure were monitored for an embroidered textile electrode composed of multifilament hybrid thread for its application as a surface electromyography (sEMG) sensor. The effect of the textile's inter-electrode distance and double layering of embroidery that increases the density of the conductive threads were studied. Electrodes embroidered onto an elastic strap were wrapped around the forearm with a hook and loop fastener and tested for their performance. Time domain features such as the Root Mean Square (RMS), Average Rectified Value (ARV), and Signal to Noise Ratio (SNR) were quantitatively monitored in relation to the contact pressure and load. Experiments were performed in triplicates, and the sEMG signal characteristics were observed for various loads (0, 2, 4, and 6 kg) and holding contact pressures (5, 10, and 20 mmHg). sEMG signals recorded with textile electrodes were comparable in amplitude to those recorded using typical Ag/AgCl electrodes (28.45 dB recorded), while the signal-to-noise ratios were, 11.77, 19.60, 19.91, and 20.93 dB for the different loads, and 21.33, 23.34, and 17.45 dB for different holding pressures. The signal quality increased as the elastic strap was tightened further, but a pressure higher than 20 mmHg is not recommended because of the discomfort experienced by the subjects during data collection.

Effects of Law Enforcement Load Carriage Systems on Muscle Activity and Coordination during Walking: An Exploratory Study.

Law enforcement officers (LEOs) commonly wear a duty belt (DB) or tactical vest (TV) and from prior findings, these forms of load carriage (LC) likely alter muscular activity. However, studies on the effects of LEO LC on muscular activity and coordination are limited in the current literature. The present study examined the effects of LEO load carriage on muscular activity and coordination. Twenty-four volunteers participated in the study (male = 13, age = 24.5 ± 6.0 years). Surface electromyography (sEMG) sensors were placed on the vastus lateralis, biceps femoris, multifidus, and lower rectus abdominus. Participants completed treadmill walking for two load carriage conditions (duty belt and tactical vest) and a control condition. Mean activity, sample entropy and Pearson correlation coefficients were computed for each muscle pair during the trials. The duty belt and tactical vest resulted in an increase in muscle activity in several muscles; however, no differences between the duty belt and tactical vest were found. Consistently across the conditions, the largest correlations were observed between the left and right multifidus (r = 0.33-0.68) and rectus abdominus muscles (0.34-0.55). There were statistically small effects (p < 0.05, η2 = 0.031 to 0.076) of the LC on intermuscular coordination. No effect (p > 0.05) of the LC on sample entropy was found for any muscle. The findings indicate that LEO LC causes small differences in muscular activity and coordination during walking. Future research should incorporate heavier loads and longer durations.