Continuous monitoring of electromyography (EMG), mechanomyography (MMG), sonomyography (SMG) and torque output during ramp and step isometric contractions

AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility

The role of the human ankle joint in activities of daily living, including walking, maintaining balance, and participating in sports, is of paramount importance. Ankle joint dorsiflexion and plantarflexion functionalities mainly account for ground clearance and propulsion power generation during locomotion tasks, where those functionalities are driven by the contraction of ankle joint skeleton muscles. Studies of corresponding muscle contractility during ankle dynamic functions will facilitate us to better understand the joint torque/power generation mechanism, better diagnose potential muscular disorders on the ankle joint, or better develop wearable assistive/rehabilitative robotic devices that assist in community ambulation. This data descriptor reports a new dataset that includes the ankle joint kinematics/kinetics, associated muscle surface electromyography, and ultrafast ultrasound images with various annotations, such as pennation angle, fascicle length, tissue displacements, echogenicity, and muscle thickness, of ten healthy participants when performing volitional isometric, isokinetic, and dynamic ankle joint functions (walking at multiple treadmill speeds, including 0.50 m/s, 0.75 m/s, 1.00 m/s, 1.25 m/s, and 1.50 m/s). Data were recorded by a research-use ultrasound machine, a self-designed ankle testbed, an inertia measurement unit system, a Vicon motion capture system, a surface electromyography system, and an instrumented treadmill. The descriptor in this work presents the results of a data curation or collection exercise from previous works, rather than describing a novel primary/experimental data collection.

High-Intensity Cycling Training Necessitates Increased Neuromuscular Demand of the Vastus Lateralis During a Fatiguing Contraction

Purpose: To examine the effects of a 5-week continuous cycling training intervention on electromyographic amplitude (EMGRMS)- and mechanomyographic amplitude (MMGRMS)-torque relationships of the vastus lateralis (VL) during a prolonged contraction. Methods: Twenty-four sedentary, young adults performed maximal voluntary contractions (MVCs) and a prolonged isometric trapezoidal contraction at the same absolute 40% MVC for the knee extensors before (PRE) and after training (POSTABS). Individual b- (slopes) and a-terms (y-intercepts) were calculated from the log-transformed electromyographic amplitude (EMGRMS)- and mechanomyographic amplitude (MMGRMS)-torque relationships during the increasing and decreasing segments of the trapezoid. EMGRMS and MMGRMS was normalized for the 45-s steady torque segment. Results: At PRE, b-terms for the EMGRMS-torque relationships during the linearly decreasing segment were greater than the increasing segment (p < .001), and decreased from PRE to POSTABS (p = .027). a-terms were greater during the linearly increasing than decreasing segment at PRE, while the a-terms for the linearly decreasing segment increased from PRE to POSTABS (p = .027). For the MMGRMS-torque relationships, b-terms during the linearly decreasing segment decreased from PRE to POSTABS (p = .013), while a-terms increased from PRE to POSTABS when collapsed across segments (p = .022). Steady torque EMGRMS increased for POSTABS (p < .001). Conclusion: Although cycling training increased aerobic endurance, incorporating resistance training may benefit athletes/individuals as the alterations in neuromuscular parameters post-training suggest a greater neural cost (EMGRMS) and mechanical output (MMGRMS) to complete the same pre-training fatiguing contraction.

A Sonomyography-Based Muscle Computer Interface for Individuals With Spinal Cord Injury

Impairment of hand functions in individuals with spinal cord injury (SCI) severely disrupts activities of daily living. Recent advances have enabled rehabilitation assisted by robotic devices to augment the residual function of the muscles. Traditionally, electromyography-based muscle activity sensing interfaces have been utilized to sense volitional motor intent to drive robotic assistive devices. However, the dexterity and fidelity of control that can be achieved with electromyography-based control have been limited due to inherent limitations in signal quality. We have developed and tested a muscle-computer interface (MCI) utilizing sonomyography to provide control of a virtual cursor for individuals with motor-incomplete spinal cord injury. We demonstrate that individuals with SCI successfully gained control of a virtual cursor by utilizing contractions of muscles of the wrist joint. The sonomyography-based interface enabled control of the cursor at multiple graded levels demonstrating the ability to achieve accurate and stable endpoint control. Our sonomyography-based muscle-computer interface can enable dexterous control of upper-extremity assistive devices for individuals with motor-incomplete SCI.

Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors

On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG.

A Novel Mechanomyography (MMG) Sensor Based on Piezo-Resistance Principle and with a Pyramidic Microarray

Flexible piezoresistive sensors built by printing nanoparticles onto soft substrates are crucial for continuous health monitoring and wearable devices. In this study, a mechanomyography (MMG) sensor was developed using a flexible piezoresistive MMG signal sensor based on a pyramidal polydimethylsiloxane (PDMS) microarray sprayed with carbon nanotubes (CNTs). The experiment was conducted, and the results show that the sensitivity of the sensor can reach 0.4 kPa-1 in the measurement range of 0~1.5 kPa, and the correlation reached 96%. This has further implications for the possibility that muscle activation can be converted into mechanical movement. The integrity of the sensor in terms of its MMG signal acquisition was tested based on five subjects who were performing arm bending and arm extending movements. The results of this test were promising.

Investigating Changes in Muscle Coordination During Cycling with Soft Wearable Strain Sensors Sensitive to Muscle Deformation

Continuous monitoring of muscle coordination can provide valuable information regarding an individual's performance during physical activities. For example, changes in muscle coordination can indicate muscle fatigue during exhaustive exercise or can be used to track the rehabilitation progress of patients post-injury. Traditional methods to evaluate coordination often focus solely on measuring muscle activation with electromyography, ignoring timing changes of the resultant force produced by the activated muscle. Setups designed to evaluate force directly to study muscle coordination are often limited by either hyper-constrained settings or cost-prohibitive hardware. In this paper, we employ wearable, ultra-sensitive soft strain sensors that track muscle deformation for estimating changes in muscle coordination during cycling at different cadences and to exhaustion. The results were compared to muscle activation timing measured by electromyography and peak force timing measured by a cycle ergometer. We demonstrate that with an increase in cadence, the soft strain sensor and ergometer timing metrics align more closely than those measured by electromyography. We also demonstrate how muscle coordination is altered with the onset of fatigue during cycling to exhaustion.

Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength

The need for developing a simple and effective assessment tool for muscle mass has been increasing in a rapidly aging society. This study aimed to evaluate the feasibility of the surface electromyography (sEMG) parameters for estimating muscle mass. Overall, 212 healthy volunteers participated in this study. Maximal voluntary contraction (MVC) strength and root mean square (RMS) values of motor unit potentials from surface electrodes on each muscle (biceps brachii, triceps brachii, biceps femoris, rectus femoris) during isometric exercises of elbow flexion (EF), elbow extension (EE), knee flexion (KF), knee extension (KE) were acquired. New variables (MeanRMS, MaxRMS, and RatioRMS) were calculated from RMS values according to each exercise. Bioimpedance analysis (BIA) was performed to determine the segmental lean mass (SLM), segmental fat mass (SFM), and appendicular skeletal muscle mass (ASM). Muscle thicknesses were measured using ultrasonography (US). sEMG parameters showed positive correlations with MVC strength, SLM, ASM, and muscle thickness measured by US, but showed negative correlations with SFM. An equation was developed for ASM: ASM = -26.04 + 20.345 × Height + 0.178 × weight - 2.065 × (1, if female; 0, if male) + 0.327 × RatioRMS(KF) + 0.965 × MeanRMS(EE) (SEE = 1.167, adjusted R² = 0.934). sEMG parameters in controlled conditions may represent overall muscle strength and muscle mass in healthy individuals.

A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control

Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging's region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ( < .001) and increased the prediction coefficient of determination by 20.13% ( < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.

Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03).

Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds

Background

Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy.

Objective

The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.

Methods

Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.

Results

On average, the normalized moment prediction root mean square error was reduced by 14.58 % (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p=0.012$$\end{document}p=0.012) and 36.79 % (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}p<0.001) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction.

Conclusions

The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01061-z.

Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression

Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.

Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation

Despite the utility of musculoskeletal dynamics modeling, there exists no safe, noninvasive method of measuring in vivo muscle output force in real time - limiting both biomechanical insight into dexterous motion and intuitive control of assistive devices. In this paper, we demonstrate that muscle deformation constitutes a promising, yet unexplored signal from which to 1) infer such forces and 2) build novel device control schemes. Through a case study of the elbow joint on a preliminary cohort of 10 subjects, we show that muscle deformation (specifically, thickness change of the brachioradialis, as measured via ultrasound and tracked via optical flow) correlates well with elbow output force to an extent comparable with standard surface electromyography (sEMG) activation during varied isometric elbow contraction. We then show that, given real-time visual feedback, subjects can readily perform a trajectory tracking task using this deformation signal, and that they largely prefer this method to a comparable sEMG-based control scheme and perform the tracking task with similar accuracy. Together, these contributions illustrate muscle deformation's potential utility for both biomechanical study of individual muscle dynamics and device control, in a manner that - thanks to, unlike sEMG, the localized nature of the signal and its tight mechanistic coupling to output force - is readily extensible to multiple muscles and device degrees of freedom. To enable such future extensions, all modeling, tracking, and visualization software described in this paper, as well as all raw and processed data, have been made available on SimTK as part of the Open-Arm project (https://simtk.org/projects/openarm) for general research use.

Effect of moderate and Severe Hypoxic exposure coupled with fatigue on psychomotor vigilance testing, muscle tissue oxygenation, and muscular performance

PURPOSE

The purpose of this study is to examine the effects of fatigue on muscular performance, oxygenation saturation, and cognition following acute hypoxic exposure at Normoxia, Moderate Hypoxia (MH), and Severe Hypoxia (SH).

METHODS

Twelve males performed 3 sets of leg extensions to failure under Normoxia (FiO2: 21%), MH (Fi02: 15.4%), and SH (Fi02: 12.9%). Heart rate, peripheral oxygenation saturation, total saturation index, psychomotor vigilance testing reaction time, psychomotor vigilance error rate, maximum strength, and repetitions to failure were measured throughout each visit.

RESULTS

The primary findings indicated that MH and SH resulted in significant decreases in psychomotor vigilance test performance (MH: 388.25-427.17 ms, 0.41-0.33 error rate; SH: 398.17-445.42 ms reaction time, 0.25-1.00 error rate), absolute muscle tissue oxygen saturation (Abs-StO2) (MH:67.22% compared to SH:57.56%), but similar muscular strength, heart rate, and patterns of muscle tissue oxygen saturation responses (StO2%) during fatigue when compared to Normoxia. There was an acute decrease in the ability to remain vigilant and/or respond correctly to visual stimuli as indicated by the worsened reaction time (PVTRT) during MH (FiO2: 15.4%) and increased PVTRT and error rate (PVTE) during SH (FiO2: 12.9%) conditions.

CONCLUSIONS

Acute hypoxic exposure in the current study was not a sufficient stimuli to elicit hypoxic-related changes in HR, muscular strength (1-RM), or repetitions to failure. The SpO2 responses were hypoxic-level dependent with increasing levels of hypoxia resulting in greater and more sustained reductions in SpO2. The combined SpO2 and StO2 responses at MH and SH suggested a balance between the muscles metabolic demand remaining lower than the muscle oxygen diffusion capacity. During the SH condition, Abs-StO2 suggested greater metabolic stress than Normoxia and MH conditions during the fatiguing leg extensions. The patterns of responses for StO2% during the three sets of leg press to failure indicated that exercise is a more potent influencer to muscle oxygenation status than hypoxic conditions (FiO2: 15.4 and 12.9%).

Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model

Many rehabilitative exoskeletons use non-invasive surface electromyography (sEMG) to measure human volitional intent. However, signals from adjacent muscle groups interfere with sEMG measurements. Further, the inability to measure sEMG signals from deeply located muscles may not accurately measure the volitional intent. In this work, we combined sEMG and ultrasound (US) imaging-derived signals to improve the prediction accuracy of voluntary ankle effort. We used a multivariate linear model (MLM) that combines sEMG and US signals for ankle joint net plantarflexion (PF) moment prediction during the walking stance phase. We hypothesized that the proposed sEMG-US imaging-driven MLM would result in more accurate net PF moment prediction than sEMG-driven and US imaging-driven MLMs. Synchronous measurements including reflective makers coordinates, ground reaction forces, sEMG signals of lateral/medial gastrocnemius (LGS/MGS), and soleus (SOL) muscles, and US imaging of LGS and SOL muscles were collected from five able-bodied participants walking on a treadmill at multiple speeds. The ankle joint net PF moment benchmark was calculated based on inverse dynamics, while the net PF moment prediction was determined by the sEMG-US imaging-driven, sEMG-driven, and US imaging-driven MLMs. The findings show that the sEMG-US imaging-driven MLM can significantly improve the prediction of net PF moment during the walking stance phase at multiple speeds. Potentially, the proposed sEMG-US imaging-driven MLM can be used as a superior joint motion intent model in advanced and intelligent control strategies for rehabilitative exoskeletons.

A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study

For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.

Biomechanics of lower limb in badminton lunge: a systematic scoping review

BACKGROUND

Badminton is a popular sport activity in both recreational and elite levels. A lot of biomechanical studies have investigated badminton lunge, since good lunge performance may increase the chances to win the game. This review summarized the current trends, research methods, and parameters-of-interest concerning lower-extremity biomechanics in badminton lunges.

METHODOLOGY

Databases including Web of Science, Cochrane Library, Scopus, and PubMed were searched from the oldest available date to September 2020. Two independent authors screened all the articles and 20 articles were eligible for further review. The reviewed articles compared the differences among playing levels, footwear designs, and lunge directions/variations, using parameters including ground reaction forces, plantar pressure distribution, kinematics, and kinetics.

RESULTS

Elite badminton players demonstrated higher impact attenuation capability, more aggressive knee and ankle strategy (higher mechanical moment), and higher medial plantar load than amateur players. Footwear modifications can influence comfort perception and movement mechanics, but it remains inconclusive regarding how these may link with lunging performance. Contradicting findings in kinematics is possibly due to the variations in lunge and instructions.

CONCLUSIONS

Playing levels and shoe designs have significant effects on biomechanics in badminton lunges. Future studies can consider to use an unanticipated testing protocol and realistic movement intensity. They can study the inter-limb coordination as well as the contributions and interactions of intrinsic and extrinsic factors to injury risk. Furthermore, current findings can stimulate further research studying whether some specific footwear materials with structural design could potentially compromise impact attenuation, proprioception, and performance.

A PILOT STUDY OF MECHANOMYOGRAPHY-BASED HAND MOVEMENTS RECOGNITION EMPHASIZING ON THE INFLUENCE OF FABRICS BETWEEN SENSOR AND SKIN

Multi-channel mechanomyography (MMG) signals were acquired from the forearm when the subjects were performing eight classes of hand movements related to rehabilitation training. Ten time domain (TD) features and wavelet packet node energy (WPNE) features were extracted from each channel of MMG, and the hand movements were classified by support vector machine (SVM), extreme learning machine (ELM), linear discriminant analysis (LDA) and [Formula: see text]-nearest neighborhood (KNN) and the classifying results of three methods of collecting MMG (sensors directly on skin, sensors on cotton fabric and sensors on acrylic fiber) were compared. When all TD features were selected and SVM was adopted as the classifier, the total recognition rates of hand movements were 94.0%, 93.9% and 93.6%, respectively, of three collection methods. Using ELM can obtain similar results as SVM, with the recognition rates of 94.3%, 94.3% and 94.1%, respectively, better than using LDA (88.5%, 88.6% and 88.0%) or KNN (88.9%, 89.4% and 89.0%). For each algorithm, using TD features can acquire the highest recognition rates. Once the feature set and the classifier were selected, the total recognition rates were almost equally among three collection methods (especially for some feature sets, the differences are smaller than 1%). The results confirmed that satisfactory effects could be acquired even when the MMG was collected from sensors on fabrics with specific material, thus indicating that MMG has a unique potential value for developing wearable devices.

Evaluation of Non-Invasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging

OBJECTIVE

Reliable measurement of voluntary human effort is essential for effective and safe interaction between the wearer and an assistive robot. Existing voluntary effort prediction methods that use surface electromyography (sEMG) are susceptible to prediction inaccuracies due to non-selectivity in measuring muscle responses. This technical challenge motivates an investigation into alternative non-invasive effort prediction methods that directly visualize the muscle response and improve effort prediction accuracy. The paper is a comparative study of ultrasound imaging (US)-derived neuromuscular signals and sEMG signals for their use in predicting isometric ankle dorsiflexion moment. Furthermore, the study evaluates the prediction accuracy of model-based and model-free voluntary effort prediction approaches that use these signals.

METHODS

The study evaluates sEMG signals and three US imaging-derived signals: pennation angle, muscle fascicle length, and echogenicity and three voluntary effort prediction methods: linear regression (LR), feedforward neural network (FFNN), and Hill-type neuromuscular model (HNM).

RESULTS

In all the prediction methods, pennation angle and fascicle length significantly improve the prediction accuracy of dorsiflexion moment, when compared to echogenicity. Also, compared to LR, both FFNN and HNM improve dorsiflexion moment prediction accuracy.

CONCLUSION

The findings indicate FFNN or HNM approach and using pennation angle or fascicle length predict human ankle movement intent with higher accuracy.

SIGNIFICANCE

The accurate ankle effort prediction will pave the path to safe and reliable robotic assistance in patients with drop foot.

Automatic Detection of Contracting Muscle Regions via the Deformation Field of Transverse Ultrasound Images: A Feasibility Study

Accurate identification of contracting muscles can help us to understand the muscle function in both physiological and pathological conditions. Conventional electromyography (EMG) have limited access to deep muscles, crosstalk, or instability in the recordings. Accordingly, a novel framework was developed to detect contracting muscle regions based on the deformation field of transverse ultrasound images. We first estimated the muscle movements in a stepwise calculation, to derive the deformation field. We then calculated the divergence of the deformation field to locate the expanding or shrinking regions during muscle contractions. Two preliminary experiments were performed to evaluate the feasibility of the developed algorithm. Using concurrent intramuscular EMG recordings, Experiment I verified that the divergence map can capture the activity of superficial and deep muscles, when muscles were activated voluntarily or through electrical stimulation. Experiment II verified that the divergence map can only capture contracting muscles but not muscle shortening during passive movements. The results demonstrated that the divergence can individually capture the activity of muscles at different depths, and was not sensitive to muscle shortening during passive movements. The proposed framework can automatically detect the regions of contracting muscle, and could potentially serve as a tool to assess the functions of a group of muscles concurrently.

Early initiation of home-based sensori-motor training improves muscle strength, activation and size in patients after knee replacement: a secondary analysis of a controlled clinical trial

Background

There is accumulating evidence for the advantages of rehabilitation involving sensori-motor training (SMT) following total knee replacement (TKR). However, the best way in which to deliver SMT remains elusive because of potential interference effects amongst concurrent exercise stimuli for optimal neuromuscular and morphological adaptations. The aim of this study was to use additional outcomes (i.e. muscle strength, activation and size) from a published parent study to compare the effects of early-initiated home-based rehabilitative SMT with functional exercise training (usual care) in patients undergoing TKR.

Methods

A controlled clinical trial was conducted at the Orthopedic University Hospital of Rion, Greece involving allocation concealment to patients. Fifty-two patients electing to undergo TKR were randomised to either early-initiated SMT [experimental] or functional exercise training [control] in a home-based environment. Groups were prescribed equivalent duration of exercise during 12-weeks, 3–5 sessions of ~ 40 min per week of home-based programmes. Muscle strength and activation (peak force [PF]; peak amplitude [Peak Amp.] and root mean square of integrated electromyography [RMS iEMG]), muscular size (including rectus femoris muscle cross-sectional area [CSA_RF]), and knee ROM were assessed on three separate occasions (pre-surgery [0 weeks]; 8 weeks post-surgery; 14 weeks post-surgery).

Results

Patients undertaking SMT rehabilitation showed significantly greater improvements over the 14 weeks compared to control in outcomes including quadriceps PF (25.1 ± 18.5 N vs 12.4 ± 20.8 N); iPeak Amp. (188 ± 109.5% vs 25 ± 105.8%); CSA_RF (252.0 ± 101.0 mm² vs 156.7 ± 76.2 mm²), respectively (p < 0.005); Knee ROM did not offer clinically relevant changes (p: ns) between groups over time. At 14 weeks post-surgery, the SMT group’s and control group’s performances differed by relative effect sizes (Cohen’s d) ranging between 0.64 and 1.06.

Conclusion

A prescribed equivalent time spent in SMT compared to usual practice, delivered within a home-based environment, elicited superior restoration of muscle strength, activation and size in patients following TKR.

Trial registration

ISRCTN12101643, December 2017 (retrospective registration).

Electronic supplementary material

The online version of this article (10.1186/s12891-019-2575-3) contains supplementary material, which is available to authorized users.

The Piezo-resistive MC Sensor is a Fast and Accurate Sensor for the Measurement of Mechanical Muscle Activity

A piezo-resistive muscle contraction (MC) sensor was used to assess the contractile properties of seven human skeletal muscles (vastus medialis, rectus femoris, vastus lateralis, gastrocnemius medialis, biceps femoris, erector spinae) during electrically stimulated isometric contraction. The sensor was affixed to the skin directly above the muscle centre. The length of the adjustable sensor tip (3, 4.5 and 6 mm) determined the depth of the tip in the tissue and thus the initial pressure on the skin, fatty and muscle tissue. The depth of the tip increased the signal amplitude and slightly sped up the time course of the signal by shortening the delay time. The MC sensor readings were compared to tensiomyographic (TMG) measurements. The signals obtained by MC only partially matched the TMG measurements, largely due to the faster response time of the MC sensor.

In Vivo Flattening of the Central Aponeurosis of the Rectus Femoris Due to Knee Extension Torque in Healthy Young and Elderly Individuals With Knee Osteoarthritis

ABSTRACT

We aimed to elucidate the relationship between active force production and the curvature of the central aponeurosis (CA) of the rectus femoris in young healthy participants as fundamental data and compare the muscle CA curvature before and after straight leg raising (SLR) training in participants with knee osteoarthritis (OA). Central aponeurosis curvature was determined during submaximal and maximal voluntary contractions (MVCs) using ultrasonography. Twenty-five young healthy female volunteers underwent ultrasonographic measurements under conditions of isometric MVC. They were divided into a flat shaped CA group (flat) and an incompletely flat shaped CA group (remnant). Central aponeurosis curvature was calculated as the ratio of CA height and length in the axial view. Central aponeurosis shape and muscular strength before and after muscle training were measured in 11 participants with knee OA. In the young healthy individuals, maximal voluntary torque and changes in CA curvature were significantly higher in the flat group than in the remnant group (2.15 Nm/kg and - 17.7% vs 1.75 Nm/kg and -9.8%, respectively; P = 0.005). The rate of change of the CA curvature during contraction was significantly correlated with maximal voluntary torque corrected for body mass (r = 0.512). The CA curvature progressively decreased as %MVC increased. In the OA group, CA curvature during MVC after SLR training was significantly lower than that before SLR training (3.2% vs 7.2%; P = 0.031). Central aponeurosis curvature was associated with muscle strength, and the results supported our hypothesis that geometric observation of CA changes during contractions may reflect muscle fiber function. We aim to develop a new ultrasonographic skeletal muscle evaluation method based on our present findings.

A Piezoresistive Sensor to Measure Muscle Contraction and Mechanomyography

Measurement of muscle contraction is mainly achieved through electromyography (EMG) and is an area of interest for many biomedical applications, including prosthesis control and human machine interface. However, EMG has some drawbacks, and there are also alternative methods for measuring muscle activity, such as by monitoring the mechanical variations that occur during contraction. In this study, a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR) which is able to measure muscle contraction is presented. The sensor, applied on the skin through a rigid dome, senses the mechanical force exerted by the underlying contracting muscles. Although FSR creep causes output drift, it was found that appropriate FSR conditioning reduces the drift by fixing the voltage across the FSR and provides voltage output proportional to force. In addition to the larger contraction signal, the sensor was able to detect the mechanomyogram (MMG), i.e., the little vibrations which occur during muscle contraction. The frequency response of the FSR sensor was found to be large enough to correctly measure the MMG. Simultaneous recordings from flexor carpi ulnaris showed a high correlation (Pearson's r > 0.9) between the FSR output and the EMG linear envelope. Preliminary validation tests on healthy subjects showed the ability of the FSR sensor, used instead of the EMG, to proportionally control a hand prosthesis, achieving comparable performances.

Investigation of the Relationship Between Electrical Stimulation Frequency and Muscle Frequency Response Under Submaximal Contractions

Neuromuscular electrical stimulation (NMES) is a common tool that is used in clinical and laboratory experiments and can be combined with mechanomyography (MMG) for biofeedback in neuroprostheses. However, it is not clear if the electrical current applied to neuromuscular tissues influences the MMG signal in submaximal contractions. The objective of this study is to investigate whether the electrical stimulation frequency influences the mechanomyographic frequency response of the rectus femoris muscle during submaximal contractions. Thirteen male participants performed three maximal voluntary isometric contractions (MVIC) recorded in isometric conditions to determine the maximal force of knee extensors. This was followed by the application of nine modulated NMES frequencies (20, 25, 30, 35, 40, 45, 50, 75, and 100 Hz) to evoke 5% MVIC. Muscle behavior was monitored by the analysis of MMG signals, which were decomposed into frequency bands by using a Cauchy wavelet transform. For each applied electrical stimulus frequency, the mean MMG spectral/frequency response was estimated for each axis (X, Y, and Z axes) of the MMG sensor with the values of the frequency bands used as weights (weighted mean). Only with respect to the Z (perpendicular) axis of the MMG signal, the stimulus frequency of 20 Hz did not exhibit any difference with the weighted mean (P = 0.666). For the frequencies of 20 and 25 Hz, the MMG signal displayed the bands between 12 and 16 Hz in the three axes (P < 0.050). In the frequencies from 30 to 100 Hz, the muscle presented a higher concentration of the MMG signal between the 22 and 29 Hz bands for the X and Z axes, and between 16 and 34 Hz bands for the Y axis (P < 0.050 for all cases). We observed that MMG signals are not dependent on the applied NMES frequency, because their frequency contents tend to mainly remain between the 20- and 25-Hz bands. Hence, NMES does not interfere with the use of MMG in neuroprosthesis.

Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal's amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects' RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm. Graphical abstract ᅟ.

A Bio-mechanical Model for Elbow Isokinetic and Isotonic Flexions

A new bio-mechanical model for elbow flexions is proposed to quantify the elbow torque generated as a function of the upper-arm circumferential strain and influencing factors of elbow angle and angular velocity. The upper-arm circumferential strain is used to represent the contractile intensity of the dominant flexor, biceps brachii, whose behavior is described by Hill's theory. Experiments with thirteen healthy subjects were conducted to determine the influencing factors. The temporal distributions of torque and elbow angle were measured by Biodex ®3 simultaneously, while the upper-arm circumference was obtained by a wearable anthropometric measurement device. Within the experimental range, the change of angular velocity has been found to have no effect on the torque generated. The new model was further verified experimentally with reasonable agreements obtained. The mean relative error of the torque estimated from the model is 15% and 22%, for isokinetic and isotonic flexions, respectively. The verified model establishes the relationship between the torque generated and circumference strain of the upper arm, for the first time, thus provide a scientific foundation for the anthropometric measurement technology as an alternative to sEMG for monitoring force/torque generation during elbow flexions.

A more precise, repeatable and diagnostic alternative to surface electromyography - an appraisal of the clinical utility of acoustic myography

Acoustic myography (AMG) enables a detailed and accurate measurement of those muscles involved in a particular movement and is independent of electrical signals between the nerve and muscle, measuring solely muscle contractions, unlike surface electromyography (sEMG). With modern amplifiers and digital sound recording systems, measurements during physical activity both inside and outside a laboratory setting are now possible and accurate. Muscle sound gives a representation of the work of each muscle group during a complex movement, and under certain forms of movement even reveals both concentric and eccentric activity, something that sEMG is incapable of. Recent findings suggest that AMG has a number of advantages over sEMG, being simple to use, accurate and repeatable as well as being intuitive to interpret. The AMG signal comprises three physiological parameters, namely efficiency/coordination (E-score), spatial summation (S-score) and temporal summation (T-score). It is concluded that modern AMG units have the potential to accurately assess patients with neuromuscular and musculoskeletal complaints in hospital clinics, home monitoring situations as well as sports settings.

The neuromotor effects of transverse friction massage

BACKGROUND

Transverse friction massage (TFM), as an often used technique by therapists, is known for its effect in reducing the pain and loosing the scar tissues. Nevertheless, its effects on neuromotor driving mechanism including the electromechanical delay (EMD), force transmission and excitation-contraction (EC) coupling which could be used as markers of stiffness changes, has not been computed using ultrafast ultrasound (US) when combined with external sensors.

AIM

Hence, the aim of this study was to find out produced neuromotor changes associated to stiffness when TFM was applied over Quadriceps femoris (QF) tendon in healthy subjcets.

METHODS

Fourteen healthy males and fifteen age-gender matched controls were recruited. Surface EMG (sEMG), ultrafast US and Force sensors were synchronized and signals were analyzed to depict the time delays corresponding to EC coupling, force transmission, EMD, torque and rate of force development (RFD).

RESULTS

TFM has been found to increase the time corresponding to EC coupling and EMD, whilst, reducing the time belonging to force transmission during the voluntary muscle contractions.

CONCLUSIONS

A detection of the increased time of EC coupling from muscle itself would suggest that TFM applied over the tendon shows an influence on changing the neuro-motor driving mechanism possibly via afferent pathways and therefore decreasing the active muscle stiffness. On the other hand, detection of decreased time belonging to force transmission during voluntary contraction would suggest that TFM increases the stiffness of tendon, caused by faster force transmission along non-contractile elements. Torque and RFD have not been influenced by TFM.

The influence of myosin heavy chain isoform content on mechanical behavior of the vastus lateralis in vivo

This study examined correlations between type I percent myosin heavy chain isoform content (%MHC) and mechanomyographic amplitude (MMGRMS) during isometric muscle actions. Fifteen (age=21.63±2.39) participants performed 40% and 70% maximal voluntary contractions (MVC) of the leg extensors that included increasing, steady force, and decreasing segments. Muscle biopsies were collected and MMG was recorded from the vastus lateralis. Linear regressions were fit to the natural-log transformed MMGRMS-force relationships (increasing and decreasing segments) and MMGRMS was selected at the targeted force level during the steady force segment. Correlations were calculated among type I%MHC and the b (slopes) terms from the MMGRMS-force relationships and MMGRMS at the targeted force. For the 40% MVC, correlations were significant (P<0.02) between type I%MHC and the b terms from the increasing (r=-0.804) and decreasing (r=-0.568) segments, and MMGRMS from the steady force segment (r=-0.606). Type I%MHC was only correlated with MMGRMS during the steady force segment (P=0.044, r=-0.525) during the 70% MVC. Higher type I%MHC reduced acceleration in MMGRMS (b terms) during the 40% MVC and the amplitude during the steady force segments. The surface MMG signal recorded during a moderate intensity contraction provided insight on the contractile properties of the VL in vivo.

Mechanomyographic mean power frequency during an isometric trapezoid muscle action at multiple contraction intensities

This study examined the mechanomyographic mean power frequency (MMGMPF)-force relationships for five (age = 19.20   ±   0.45 years) aerobically-trained (AT), five (age = 25   ±   4.53 years) resistance-trained (RT), and five (age = 21.20   ±   2.17 years) sedentary (SED) individuals. Participants performed isometric trapezoidal muscle actions at 50, 60, and 70% maximal voluntary contraction (MVC) of the leg extensors that included linearly increasing, steady force, and linearly decreasing segments. MMG was recorded from the vastus lateralis. Linear regressions were fit to the natural-log transformed MMGMPF versus natural log-transformed force relationships (linearly increasing and decreasing segments) with the b (slope) and a (y-intercept) terms used for comparisons. MMGMPF was averaged for the entire steady force segment. The b and a terms were not different among training statuses (P  >  0.05) or linearly increasing and decreasing segments (P  >  0.05). There were muscle action-related differences in the b terms as a function of training status from the 70% MVC. The SED had greater b terms during the linearly increasing than decreasing muscle action (P = 0.010), and the converse was true for the AT (P = 0.013), whereas the RT displayed no muscle action-related differences (P  >  0.05). The unique muscle action-related differences in the b terms as a function of training status may be the result of unique adaptations to motor unit activation and deactivation strategies.

The influence of electromyographic recording methods and the innervation zone on the mean power frequency-torque relationships

This study examined the effects of electromyographic (EMG) recording methods and innervation zone (IZ) on the mean power frequency (MPF)-torque relationships. Nine subjects performed isometric ramp muscle actions of the leg extensors from 5% to 100% of maximal voluntary contraction with an eight channel linear electrode array over the IZ of the vastus lateralis. The slopes were calculated from the log-transformed monopolar and bipolar EMG MPF-torque relationships for each channel and subject and 95% confidence intervals (CI) were constructed around the slopes for each relationship and the composite of the slopes. Twenty-two to 55% of the subjects exhibited 95% CIs that did not include a slope of zero for the monopolar EMG MPF-torque relationships while 25-75% of the subjects exhibited 95% CIs that did not include a slope of zero for the bipolar EMG MPF-torque relationships. The composite of the slopes from the EMG MPF-torque relationships were not significantly different from zero for any method or channel, however, the method and IZ location slightly influenced the number of significant slopes on a subject-by-subject basis. The log-transform model indicated that EMG MPF-torque patterns were nonlinear regardless of recording method or distance from the IZ.

Estimation and visualization of longitudinal muscle motion using ultrasonography: a feasibility study

Ultrasonography is a convenient and widely used technique to look into the longitudinal muscle motion as it is radiation-free and real-time. The motion of localized parts of the muscle, disclosed by ultrasonography, spatially reflects contraction activities of the corresponding muscles. However, little attention was paid to the estimation of longitudinal muscle motion, especially towards estimation of dense deformation field at different depths under the skin. Yet fewer studies on the visualization of such muscle motion or further clinical applications were reported in the literature. A primal-dual algorithm was used to estimate the motion of gastrocnemius muscle (GM) in longitudinal direction in this study. To provide insights into the rules of longitudinal muscle motion, we proposed a novel framework including motion estimation, visualization and quantitative analysis to interpret synchronous activities of collaborating muscles with spatial details. The proposed methods were evaluated on ultrasound image sequences, captured at a rate of 25 frames per second from eight healthy subjects. In order to estimate and visualize the GM motion in longitudinal direction, each subject was asked to perform isometric plantar flexion twice. Preliminary results show that the proposed visualization methods provide both spatial and temporal details and they are helpful to study muscle contractions. One of the proposed quantitative measures was also tested on a patient with unilateral limb dysfunction caused by cerebral infarction. The measure revealed distinct patterns between the normal and the dysfunctional lower limb. The proposed framework and its associated quantitative measures could potentially be used to complement electromyography (EMG) and torque signals in functional assessment of skeletal muscles.

Electromyography and sonomyography analysis of the tibialis anterior: a cross sectional study

Background

Foot dorsiflexion plays an essential role in both controlling balance and human gait. Electromyography (EMG) and sonomyography (SMG) can provide information on several aspects of muscle function. The aim was to establish the relationship between the EMG and SMG variables during isotonic contractions of foot dorsiflexors.

Methods

Twenty-seven healthy young adults performed the foot dorsiflexion test on a device designed ad hoc. EMG variables were maximum peak and area under the curve. Muscular architecture variables were muscle thickness and pennation angle. Descriptive statistical analysis, inferential analysis and a multivariate linear regression model were carried out. The confidence level was established with a statistically significant p-value of less than 0.05.

Results

The correlation between EMG variables and SMG variables was r = 0.462 (p < 0.05). The linear regression model to the dependent variable “peak normalized tibialis anterior (TA)” from the independent variables “pennation angle and thickness”, was significant (p = 0.002) with an explained variance of R² = 0.693 and SEE = 0.16.

Conclusions

There is a significant relationship and degree of contribution between EMG and SMG variables during isotonic contractions of the TA muscle. Our results suggest that EMG and SMG can be feasible tools for monitoring and assessment of foot dorsiflexors. TA muscle parameterization and assessment is relevant in order to know that increased strength accelerates the recovery of lower limb injuries.

Relationships between skinfold thickness and electromyographic and mechanomyographic amplitude recorded during voluntary and non-voluntary muscle actions

INTRODUCTION

The purpose of this study was to examine possible correlations between skinfold thicknesses and the a terms from the log-transformed electromyographic (EMGRMS) and mechanomyographic amplitude (MMGRMS)-force relationships, EMG M-Waves, and MMG gross lateral movements (GLM).

METHODS

Forty healthy subjects performed a 6-s isometric ramp contraction from 5% to 85% of their maximal voluntary contraction with EMG and MMG sensors placed on the vastus lateralis (VL) and rectus femoris (RF). A single electrical stimulus was applied to the femoral nerve to record the EMG M-waves and MMG GLMs. Skinfold thickness was assessed at the site of each electrode. Pearson's product correlation coefficients were calculated comparing skinfold thicknesses with the a terms from the log-transformed EMGRMS-and MMGRMS-force relationships, EMG M-waves, and MMG GLMs.

RESULTS

There were no significant cor1relations (p>0.05) between the a terms and skinfold thicknesses for the RF and VL from the EMGRMS and MMGRMS-force relationships. However, there were significant correlations (p<0.05) between skinfold thicknesses and the EMG M-waves and MMG GLMs for the RF (r=-0.521, -0.376) and VL (r=-0.479, -0.484).

DISCUSSION

Relationships were only present between skinfold thickness and the amplitudes of the EMG and MMG signals during the non-voluntary muscle actions.

Relationship of moderate and low isometric lumbar extension through architectural and muscular activity variables: a cross sectional study

BACKGROUND

No study relating the changes obtained in the architecture of erector spinae (ES) muscle were registered with ultrasound and different intensities of muscle contraction recorded by surface EMG (electromyography) on the ES muscle was found. The aim of this study was analyse the relationship in the response of the ES muscle during isometric moderate and light lumbar isometric extension considering architecture and functional muscle variables.

METHODS

Cross-sectional study. 46 subjects (52% men) with a group mean age of 30.4 (±7.78). The participants developed isometric lumbar extension while performing moderate and low isometric trunk and hip extension in a sitting position with hips flexed 90 degrees and the lumbar spine in neutral position. During these measurements, electromyography recordings and ultrasound images were taken bilaterally. Bilaterally pennation angle, muscle thickness, torque and muscle activation were measured. This study was developed at the human movement analysis laboratory of the Health Science Faculty of the University of Malaga (Spain).

RESULTS

Strong and moderate correlations were found at moderate and low intensities contraction between the variable of the same intensity, with correlation values ranging from 0.726 (Torque Moderate - EMG Left Moderate) to 0.923 (Angle Left Light - Angle Right Light) (p < 0.001). This correlation is observed between the variables that describe the same intensity of contraction, showing a poor correlation between variables of different intensities.

CONCLUSION

There is a strong relationship between architecture and function variables of ES muscle when describe an isometric lumbar extension at light or moderate intensity.

Portable acoustic myography - a realistic noninvasive method for assessment of muscle activity and coordination in human subjects in most home and sports settings

Muscle sound gives a local picture of muscles involved in a particular movement and is independent of electrical signals between nerve and muscle. Sound recording (acoustic myography) is a well-known noninvasive technique that has suffered from not being easily applicable, as well as not being able to register at sufficient sampling speed. With modern amplifiers and digital sound recording this has changed, and such assessment during movement outside a laboratory setting may be possible. Our aim was to develop a setup for muscle-sound assessment, which could be reliably applied in any local setting. A group of healthy subjects were assessed during standing, stair climbing, walking, and running. Piezoelectric microphones were applied to the skin using contact gel. A digital sound recorder enabled sampling speeds of around 96,000 Hz. Surface electromyography was measured in parallel as a comparison. The recorded signals were assessed and described in terms of signal frequency (Hz) and peak-to-peak amplitude (mV) using Chart software. Bioimpedance of the involved muscles was measured. Sound recording was shown to be an easy noninvasive method for assessment of muscle function during movement with the possibility of being applied in most clinical, sports, and home settings. Muscle sound gives a representation of the work of each muscle group during a complex movement, illustrated here by a step test, which revealed both concentric and eccentric activity. The method in the presented new setup has great potential for assessment of function in patients with musculoskeletal complaints in out-of-clinic settings, as well as in sports.