Continuous monitoring of sonomyography, electromyography and torque generated by normal upper arm muscles during isometric contraction: sonomyography assessment for arm muscles

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.

Wearable Ultrasound Imaging Device for Dynamic Dual-Direction Shear Wave Elastography of Shoulder Muscle

The shoulder is the most mobile joint in the human body, thus requiring intricate coordination of adjacent muscles. Patients suffered from rotator cuff muscle injuries have several typical symptoms including shoulder pain and difficulty raising the arm, thus reducing work efficiency and compromising the quality of life. Ultrasound has been used widely for shoulder soft tissue imaging as well as ultrasound elastography was introduced in shoulder examination for the dilemma of treating degenerative rotator cuff tears. However, most of the ultrasound examination was performed under a static condition. Providing dynamic information from shoulder muscle is important in clinical applications because the pains sometimes come from various positions of the shoulder during moving. In this study, a customized wearable T-shaped ultrasound transducer (128 + 128 elements) was proposed for shoulder dual-direction shear wave elastography (DDSWE), which provides the SWE for both longitudinal (SW along the muscle fiber) and transverse (SW cross the muscle fiber) directions dynamically. An optical tracking system was synchronized with an ultrasound imaging system to capture shoulder movements in 3-D space with their corresponding ultrasound images. The performance of DDSWE and the accuracy of optical tracking were verified by phantom experiments. Human studies were carried out by volunteers as they are moving their arms. The experimental results show that the bias and precision for the proposed DDSWE in elastic phantom were about 6% and 1.2% for both directions, respectively. A high accuracy of optical tracking was observed using a 3-D motor stage experimental setup. Human experiments show that the shear wave velocities (SWVs) were increased with the angles of shoulder abduction, and the average transverse and longitudinal SWVs were increased from 2.24 to 3.35 m/s and 2.95 to 5.95 m/s with abduction angle from 0° to 60°, respectively, which they are anisotropic-dependent. All the experimental results indicate that the proposed wearable ultrasound DDSWE can quantify the mechanical properties of shoulder muscles dynamically, thereby helping surgeons and physical therapists determine whether the intensity of rehabilitation shoulder be tuned down or escalated in the future.

SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force from Highly Sparse Ultrasound Images

Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared to surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches for tracking muscle anatomical structures or extracting features from brightness-mode (B-mode) images and amplitude-mode (A-mode) signals. This paper uses an offline regression convolutional neural network (CNN) called SonoMyoNet to estimate continuous isometric force from sparse ultrasound scanlines. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to estimate continuous isometric force accurately. The performance of SonoMyoNet was evaluated by varying the number of scanlines to simulate the placement of multiple single-element ultrasound transducers in a wearable system. Results showed that SonoMyoNet could accurately predict isometric force with just four scanlines and is immune to speckle noise and shifts in the scanline location. Thus, the proposed network reduces the computational load involved in feature tracking algorithms and estimates muscle force from the global features of sparse ultrasound images.

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.

Effect of Forearm Postures and Elbow Joint Angles on Elbow Flexion Torque and Mechanomyography in Neuromuscular Electrical Stimulation of the Biceps Brachii

Neuromuscular electrical stimulation plays a pivotal role in rehabilitating muscle function among individuals with neurological impairment. However, there remains uncertainty regarding whether the muscle's response to electrical excitation is affected by forearm posture, joint angle, or a combination of both factors. This study aimed to investigate the effects of forearm postures and elbow joint angles on the muscle torque and MMG signals. Measurements of the torque around the elbow and MMG of the biceps brachii (BB) muscle were conducted in 36 healthy subjects (age, 22.24 ± 2.94 years; height, 172 ± 0.5 cm; and weight, 67.01 ± 7.22 kg) using an in-house elbow flexion testbed and neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle was stimulated while the forearm was positioned in the neutral, pronation, or supination positions. The elbow was flexed at angles of 10°, 30°, 60°, and 90°. The study analyzed the impact of the forearm posture(s) and elbow joint angle(s) on the root-mean-square value of the torque (TQRMS). Subsequently, various MMG parameters, such as the root-mean-square value (MMGRMS), the mean power frequency (MMGMPF), and the median frequency (MMGMDF), were analyzed along the longitudinal, lateral, and transverse axes of the BB muscle fibers. The test-retest interclass correlation coefficient (ICC21) for the torque and MMG ranged from 0.522 to 0.828. Repeated-measure ANOVAs showed that the forearm posture and elbow flexion angle significantly influenced the TQRMS (p < 0.05). Similarly, the MMGRMS, MMGMPF, and MMGMDF showed significant differences among all the postures and angles (p < 0.05). However, the combined main effect of the forearm posture and elbow joint angle was insignificant along the longitudinal axis (p > 0.05). The study also found that the MMGRMS and TQRMS increased with increases in the joint angle from 10° to 60° and decreased at greater angles. However, during this investigation, the MMGMPF and MMGMDF exhibited a consistent decrease in response to increases in the joint angle for the lateral and transverse axes of the BB muscle. These findings suggest that the muscle contraction evoked by NMES may be influenced by the interplay between actin and myosin filaments, which are responsible for muscle contraction and are, in turn, influenced by the muscle length. Because restoring the function of limbs is a common goal in rehabilitation services, the use of MMG in the development of methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels is imperative.

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 Nonswelling Hydrogel with Regenerable High Wet Tissue Adhesion for Bioelectronics

Reducing the swelling of tissue-adhesive hydrogels is crucial for maintaining stable tissue adhesion and inhibiting tissue inflammation. However, reported strategies for reducing swelling always result in a simultaneous decrease in the tissue adhesive strength of the hydrogel. Furthermore, once the covalent bonds break in the currently reported hydrogels, they cannot be rebuilt, and the hydrogel loses its tissue adhesive ability. In this work, a nonswelling hydrogel (named as "PAACP") possessing regenerable high tissue adhesion is synthesized by copolymerizing and crosslinking poly(vinyl butyral) with acrylic acid, gelatin, and chitosan-grafted N-acetyl-l-cysteine. The tissue adhesive strength of the obtained PAACP reaches 211.4 kPa, which is approximately ten times higher than that of the reported nonswelling hydrogels, and the hydrogel can be reused for multiple cycles. The as-prepared hydrogel shows great potential in soft bioelectronics, as muscle fatigue is successfully monitored via the electrode array and strain sensor integrated on PAACP substrates. The success of these bioelectronics offers potential applicability in the long-term diagnosis of muscle-related health conditions and prosthetic manipulations.

Activation asymmetry of the lateral abdominal muscles in response to neurodevelopmental traction technique in children with pelvic asymmetry

Study aim: The aim of the study was to evaluate asymmetry of activation of lateral abdominal muscles (LAM) in response to neurodevelopmental traction technique in children with pelvic asymmetry.

Material and methods: Measurements of LAM activation asymmetry were performed during traction with the force of 5% body weight in two experimental conditions: 1) in neutral position, 2) in 20° posterior trunk inclination. Twenty-three healthy children with pelvic asymmetry participated in the study. To evaluate LAM activation asymmetry ultrasound technology was employed (two Mindray DP660 devices (Mindray, Shenzhen, China)). Activation asymmetry indices for each individual LAM were calculated.

Results: The magnitude of LAM activation asymmetry indexes formed a gradient, with the most profound transversus abdominis (TrA) showing the greatest asymmetry, and the most superficial obliquus externus – the smallest. The inter-muscle differences were most pronounced between the TrA and the two more superficial oblique muscles. There were no correlation between the magnitude of pelvic asymmetry and LAM activation asymmetry.

Conclusions: During the neurodevelopmental traction technique there is a difference in individual LAM activation symmetry, with deeper muscles showing greater asymmetry. The activation asymmetry of the LAM does not seem to be associated with the pelvic asymmetry. Results are similar to those recorded in earlier studies in samples where no pelvic asymmetry were subjected to analysis.

Wearable Real-Time Gesture Recognition Scheme Based on A-Mode Ultrasound

A-mode ultrasound has the advantages of high resolution, easy calculation and low cost in predicting dexterous gestures. In order to accelerate the popularization of A-mode ultrasound gesture recognition technology, we designed a human-machine interface that can interact with the user in real-time. Data processing includes Gaussian filtering, feature extraction and PCA dimensionality reduction. The NB, LDA and SVM algorithms were selected to train machine learning models. The whole process was written in C++ to classify gestures in real-time. This paper conducts offline and real-time experiments based on HMI-A (Human-machine interface based on A-mode ultrasound), including ten subjects and ten common gestures. To demonstrate the effectiveness of HMI-A and avoid accidental interference, the offline experiment collected ten rounds of gestures for each subject for ten-fold cross-validation. The results show that the offline recognition accuracy is 96.92% ± 1.92%. The real-time experiment was evaluated by four online performance metrics: action selection time, action completion time, action completion rate and real-time recognition accuracy. The results show that the action completion rate is 96.0% ± 3.6%, and the real-time recognition accuracy is 83.8% ± 6.9%. This study verifies the great potential of wearable A-mode ultrasound technology, and provides a wider range of application scenarios for gesture recognition.

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.

Effect of Subcutaneous Muscle Displacement of Flexor Carpi Radialis on Surface Electromyography

Changes in joint angle can change the position and orientation of muscle fibers relative to the surface EMG electrode. Our previous study has shown that EMG patterns can identify hand/wrist movements with a greater degree of classification accuracy (CA) when muscle contractions involve a change in the joint angle. The results of this study suggest that changes in the position of the muscle relative to the recording electrode can influence the properties of the recorded EMG signals, however, this was not directly quantified. The present study aims to further investigate the effect of subcutaneous muscle displacement caused by the changes in joint angle on surface EMG signals. Nine able-bodied subjects were tested. The subjects were instructed to perform wrist flexion at five different joint angles (0, 20, 40, 60, and 80) with the same level of muscle contraction. EMG signals and ultrasound images were acquired from the flexor carpi radialis (FCR) simultaneously. Time and frequency domain analysis was adopted to extract features from the EMG signals. The subcutaneous muscle displacement of the FCR relative to the skin surface was measured from the ultrasound images. Spearmans rank correlation coefficient was employed to analyze the correlation between the subcutaneous muscle displacement and the EMG signals. The results showed the subcutaneous muscle displacement of the FCR measured by the ultrasound images was 1 cm when the wrist joint angle changed from 0 to 80. There was a positive relationship between the subcutaneous muscle displacement and the mean absolute value (MAV) (rs = 0.896) and median frequency (MF) (rs = 0.849) extracted from the EMG signals. The results demonstrated that subcutaneous muscle displacement associated with wrist angle change had a significant effect on FCR EMG signals. This property might have a positive effect on the CA of dynamic tasks.

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

The lower-limb robotic prostheses can provide assistance for amputees' daily activities by restoring the biomechanical functions of missing limb(s). To set proper control strategies and develop the corresponding controller for robotic prosthesis, a prosthesis user's intent must be acquired in time, which is still a major challenge and has attracted intensive attentions. This work focuses on the robotic prosthesis user's locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective (locomotion mode recognition, gait event detection, and continuous gait phase estimation) and reviews the state-of-the-art intent recognition techniques in a lower-limb prosthesis scope. The current research status, including recognition approach, progress, challenges, and future prospects in the human's intent recognition, has been reviewed. In particular for the recognition approach, the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition. This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.

The War after War: Volumetric Muscle Loss Incidence, Implication, Current Therapies and Emerging Reconstructive Strategies, a Comprehensive Review

Volumetric muscle loss (VML) is the massive wasting of skeletal muscle tissue due to traumatic events or surgical ablation. This pathological condition exceeds the physiological healing process carried out by the muscle itself, which owns remarkable capacity to restore damages but only when limited in dimensions. Upon VML occurring, the affected area is severely compromised, heavily influencing the affected a person’s quality of life. Overall, this condition is often associated with chronic disability, which makes the return to duty of highly specialized professional figures (e.g., military personnel or athletes) almost impossible. The actual treatment for VML is based on surgical conservative treatment followed by physical exercise; nevertheless, the results, in terms of either lost mass and/or functionality recovery, are still poor. On the other hand, the efforts of the scientific community are focusing on reconstructive therapy aiming at muscular tissue void volume replenishment by exploiting biomimetic matrix or artificial tissue implantation. Reconstructing strategies represent a valid option to build new muscular tissue not only to recover damaged muscles, but also to better socket prosthesis in terms of anchorage surfaces and reinnervation substrates for reconstructed mass.

M-Mode Ultrasound Examination of Soleus Muscle in Healthy Subjects: Intra- and Inter-Rater Reliability Study

Objective: M-mode ultrasound imaging (US) reflects the motion of connective tissue within muscles. The objectives of this study were to evaluate inter-rater and intra-rater reliability of soleus muscle measurements between examiners with different levels of US experience in asymptomatic subjects and to investigate the level of soleus muscle isometric activity in two positions (knee extended and knee flexed at 30°). Methods: Thirty volunteers without a history of ankle pain were evaluated with US examinations of the soleus muscle. Each muscle was scanned independently by two evaluators. Muscle at rest thickness, maximal isometric contraction thickness, time and velocity measures were detailed and blinded to the other examiner. Results: Intra- and inter-rater reliability at rest, in maximal isometric contraction thickness, contraction time and contraction velocity measures for both positions (extended and flexed knee) were reported from good to excellent for all outcome measurements. The position with the knee extended reported a statistically significant increase in thickness after motion showing 1.33 ± 0.27 mm for measurements at rest thickness with knee extended versus 1.50 ± 0.29 mm for measurements at end thickness with the knee in flexed position (p = 0.001), as well as 1.31 ± 0.23 mm for rest thickness with the knee in flexed position measurements with respect to 1.34 ± 0.24 mm for maximal isometric contraction thickness with extended knee measurements (p = 0.058). Conclusions: This study found that intra- and inter-examiner reliability of M-mode ultrasound imaging of the soleus muscle was excellent in asymptomatic subjects and the soleus muscle activity was different between the position with the knee extended and the position with the knee flexed.

Identification of muscle morphology with noncontact capacitive sensing: Preliminary study

Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncon-tact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R² > 0.5, quadratic regression). The average R²s of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.

Sonomechanomyography (SMMG): Mapping of Skeletal Muscle Motion Onset during Contraction Using Ultrafast Ultrasound Imaging and Multiple Motion Sensors

BACKGROUND

Available methods for studying muscle dynamics, including electromyography (EMG), mechanomyography (MMG) and M-mode ultrasound, have limitations in terms of spatial resolution.

METHODS

This study developed a novel method/protocol of two-dimensional mapping of muscle motion onset using ultrafast ultrasound imaging, i.e., sono-mechano-myo-graphy (SMMG). The developed method was compared with the EMG, MMG and force outputs of tibialis anterior (TA) muscle during ankle dorsiflexion at different percentages of maximum voluntary contraction (MVC) force in healthy young adults.

RESULTS

Significant differences between all pairwise comparisons of onsets were identified, except between SMMG and MMG. The EMG onset significantly led SMMG, MMG and force onsets by 40.0 ± 1.7 ms (p < 0.001), 43.1 ± 5.2 ms (p < 0.005) and 73.0 ± 4.5 ms (p < 0.001), respectively. Muscle motion also started earlier at the middle aponeurosis than skin surface and deeper regions when viewed longitudinally (p < 0.001). No significant effect of force level on onset delay was found.

CONCLUSIONS

This study introduced and evaluated a new method/protocol, SMMG, for studying muscle dynamics and demonstrated its feasibility for muscle contraction onset research. This novel technology can potentially provide new insights for future studies of neuromuscular diseases, such as multiple sclerosis and muscular dystrophy.

Quadriceps architecture in individuals with patellofemoral pain: A systematic review

OBJECTIVES

To identify differences in architectural parameters (size, fiber/pennation angle, fiber length, and echogenicity) of the quadriceps muscle, as a whole or any individual part of it, using imaging techniques in individuals with patellofemoral pain (PFP) compared to contralateral, asymptomatic limb or separate control group.

BACKGROUND

Quadriceps muscles imbalance and weakness were proposed as risk factors for developing PFP. Although the muscle architecture (size, pennation/fiber angle and fiber length) is highly associated with skeletal muscle strength, it is not clear whether atrophy or any changes in architectural parameters of the quadriceps are presented in the PFP patients.

METHODS

Observational studies in which the total size of the quadriceps or individual parts of it were measured using imaging techniques in the PFP patients were included in this review. Electronic databases (PubMed, SCOPUS, PEDro, CINAHL, WOS, and EMBASE) were searched between January 1990 and December 2019 with no restriction of language. Study selection and data extraction and quality assessment were conducted by two independent reviewers.

RESULTS

Five cross-sectional studies were eligible to include in this review. Three out of five included studies that assessed the total size of the quadriceps and reported no significant differences between the PFP and healthy control group. Two out of five of the studies assessed the total size of the quadriceps between the symptomatic and asymptomatic limb of the PFP patients and reported the statistical difference between limbs. Only one study measured the fiber angle of the VMO muscle at the patella. Controversial results were found between studies that assessed the size of individual parts of the quadriceps.

CONCLUSION

Due to the controversial results of the included studies, this systematic review failed to draw a conclusion on the role of quadriceps atrophy in PFP pathology. The limitation in PFP literature considering pennation/fiber angle, echogenicity and fiber length of individual parts of the quadriceps muscle, rises the need for research that focuses on the biomechanical properties of the quadriceps in PFP patients.

Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback

The loss of a hand can significantly affect one's work and social life. For many patients, an artificial limb can improve their mobility and ability to manage everyday activities, as well as provide the means to remain independent. This paper provides an extensive review of available biosensing methods to implement the control system for transradial prostheses based on the measured activity in remnant muscles. Covered techniques include electromyography, magnetomyography, electrical impedance tomography, capacitance sensing, near-infrared spectroscopy, sonomyography, optical myography, force myography, phonomyography, myokinetic control, and modern approaches to cineplasty. The paper also covers combinations of these approaches, which, in many cases, achieve better accuracy while mitigating the weaknesses of individual methods. The work is focused on the practical applicability of the approaches, and analyses present challenges associated with each technique along with their relationship with proprioceptive feedback, which is an important factor for intuitive control over the prosthetic device, especially for high dexterity prosthetic hands.

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.

Dynamically Characterizing Skeletal Muscles via Acoustic Non-linearity Parameter: In Vivo Assessment for Upper Arms

It is crucial to model skeletal muscles for muscle-centered health care, such as prosthetics. Here we hypothesize that the acoustic non-linearity parameter (B/A) can be utilized to partially represent the contraction state of skeletal muscles. Although previous work commonly measured the B/A value of tissues in vitro, the present study targets the biceps brachii muscle to investigate the relationship between the B/A value and the dynamics of the elbow. Furthermore, it is proposed that a correction method based on the angular spectrum theory be applied in vivo, and the dynamic metrics of the B/A value and its feasibility be verified through an underwater experiment. Seven participants were invited for the in vivo experiment, in which elbow torque and B/A values were measured simultaneously. The non-plane reflection was approximately treated through an integral method, leading to a modified B/A value. Then, linear regression was applied to characterize the B/A-torque relationship, with the calculated coefficient of determination (R²) ranging from 0.85-0.93. Experimental results indicate that the modified B/A value of the biceps brachii correlates well with elbow torque. This study not only paves the way to dynamic measurement of the B/A value of skeletal muscles in vivo, but also confirms that B/A can be used as a more comprehensive assessment criterion for muscle functions.

Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: A case study

Surface electromyography (sEMG) has dominated upper-limb prosthesis control for decades due to its simplicity and effectiveness [1-3]. However, the inherent variability of EMG signal hinders the flexible and accurate control of advanced multi-functional prosthesis. This study is an attempt to use ultrasonography (US) as an alternative for prosthetic hand control. A type of multi-sensory module, comprising a single-element ultrasound channel and one sEMG bipolar channel, is customised to ensure a fair comparison between these two modalities. Three machine-learning-oriented approaches were adopted to evaluate the performance in motion classification based on datasets captured from a trans-radial amputee. The experimental results demonstrated that the ultrasound outperformed the sEMG in random (98.9% vs 70.4%) and enhanced-trial-wise (74.10% vs 61.83%) cross-validation, but fell behind the sEMG in trial-wise (39.47% vs 58.04%) validation that is the closest comparison to a real life prosthetic control. This study preliminarily implies that 1) A-mode ultrasound signal can be more stable than the sEMG with minimum electrode shift, but more sensitive to external interference than the sEMG; and 2) to maintain high classification accuracy, US approach may require harsher electrode fixing mechanism or advanced on-line calibration approach.

Ankle Dorsiflexion Strength Monitoring by Combining Sonomyography and Electromyography

Ankle dorsiflexion produced by Tibialis Anterior (TA) muscle contraction plays a significant role during human walking and standing balance. The weakened function or dysfunction of the TA muscle often impedes activities of daily living (ADL). Powered ankle exoskeleton is a prevalent technique to treat this pathology, and its intelligent and effective behaviors depend on human intention detection. A TA muscle contraction strength monitor is proposed to evaluate the weakness of the ankle dorsiflexion. The new method combines surface electromyography (sEMG) signals and sonomyography signals to estimate ankle torque during a voluntary isometric ankle dorsiflexion. Changes in the pennation angle (PA) are derived from the sonomyography signals. The results demonstrate strong correlations among the sonomyography-derived PA, the sEMG signal, and the measured TA muscle contraction force. Especially, the TA muscle strength monitor approximates the TA muscle strength measurement via a weighted summation of the sEMG signal and the PA signal. The new method shows an improved linear correlation with the muscle strength, compared to the correlations between the muscle strength and sole sEMG signal or sole PA signal, where the R-squared values are improved by 4.21 % and 1.99 %, respectively.

Towards Wearable A-Mode Ultrasound Sensing for Real-Time Finger Motion Recognition

It is evident that surface electromyography (sEMG) based human-machine interfaces (HMI) have inherent difficulty in predicting dexterous musculoskeletal movements such as finger motions. This paper is an attempt to investigate a plausible alternative to sEMG, ultrasound-driven HMI, for dexterous motion recognition due to its characteristic of detecting morphological changes of deep muscles and tendons. A multi-channel A-mode ultrasound lightweight device is adopted to evaluate the performance of finger motion recognition; an experiment is designed for both widely acceptable offline and online algorithms with eight able-bodied subjects employed. The experiment result presents that the offline recognition accuracy is up to 98.83% ± 0.79%. The real-time motion completion rate is 95.4% ± 8.7% and online motion selection time is 0.243 ± 0.127 s. The outcomes confirm the feasibility of A-mode ultrasound based wearable HMI and its prosperous applications in prosthetic devices, virtual reality, and remote manipulation.

Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion

Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. Methods: In ten healthy participants, the changes in muscle area and activity of gastrocnemius, plantarflexion and dorsiflexion of right leg during walking were evaluated by the developed system and the Vicon system. The existence of significant changes in a gait cycle, comparison of the ankle kinetic data captured by the developed system and the Vicon system, and test-retest reliability (evaluated by the intraclass correlation coefficient, ICC) in each channel’s data captured by the developed system were examined. Results: Moderate to good test-retest reliability of various channels of the developed system (0.512 ≤ ICC ≤ 0.988, p < 0.05), significantly high correlation between the developed system and Vicon system in ankle joint angles (0.638R ≤ 0.707, p < 0.05), and significant changes in muscle activity of gastrocnemius during a gait cycle (p < 0.05) were found. Conclusion: A newly developed wearable motion capture and measurement system with ultrasound imaging that can accurately capture the motion of one leg was evaluated in this study, which paves the way towards real-time comprehensive evaluation of muscles and joint motions during different activities in both indoor and outdoor environments.

Identification of the relationships between noncontact capacitive sensing signals and continuous grasp forces: Preliminary study

This study explores the relationships between noncontact capacitive sensing signals and continuous grasp forces. It is a crucial step towards the volitional control of robotic systems based on the noncontact sensing approach. We firstly designed a measurement system including the capacitive sensing front-ends, the grasp force sensor, the signal sampling circuits and the graphic user interface. The capacitive sensing front-end was specifically designed for human forearm signal sampling, which was worn outside of the clothes. After implementation of the system, we carried out experiments on five healthy subjects, and the sensing bands were customized with their arm shapes. The grasp force and the capacitance signals were record simultaneously when the subjects gradually increased the force according to instruction. Linear regression and quadratic regression were used to evaluate the regulated signals. For each subject, at least one channel of capacitance signals were linear correlated to the normalized grasp force with ${{R}^{2}}\ge 0.85$. We found there was inter-subject similarity on the capacitance-force relationships. Cross validation on grasp force estimation with capacitance signals were also carried out, and the average relative estimation error was about 18%. The results proved the feasibility of the noncontact capacitive sensing method for human joint force estimation.

Forearm Motion Recognition With Noncontact Capacitive Sensing

This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes.

Ultrasound-Based Sensing Models for Finger Motion Classification

Motions of the fingers are complex since hand grasping and manipulation are conducted by spatial and temporal coordination of forearm muscles and tendons. The dominant methods based on surface electromyography (sEMG) could not offer satisfactory solutions for finger motion classification due to its inherent nature of measuring the electrical activity of motor units at the skin's surface. In order to recognize morphological changes of forearm muscles for accurate hand motion prediction, ultrasound imaging is employed to investigate the feasibility of detecting mechanical deformation of deep muscle compartments in potential clinical applications. In this study, finger motion classification has been represented as subproblems: recognizing the discrete finger motions and predicting the continuous finger angles. Predefined 14 finger motions are presented in both sEMG signals and ultrasound images and captured simultaneously. Linear discriminant analysis classifier shows the ultrasound has better average accuracy (95.88%) than the sEMG (90.14%). On the other hand, the study of predicting the metacarpophalangeal (MCP) joint angle of each finger in nonperiod movements also confirms that classification method based on ultrasound achieves better results (average correlation 0.89 $\pm$ 0.07 and NRMSE 0.15 $\pm$ 0.05) than sEMG (0.81 $\pm$ 0.09 and 0.19 $\pm$ 0.05). The research outcomes evidently demonstrate that the ultrasound can be a feasible solution for muscle-driven machine interface, such as accurate finger motion control of prostheses and wearable robotic devices.

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.

Ultrasonic Measurement of Dynamic Muscle Behavior for Poststroke Hemiparetic Gait

Quantitative evaluation of the hemiparesis status for a poststroke patient is still challenging. This study aims to measure and investigate the dynamic muscle behavior in poststroke hemiparetic gait using ultrasonography. Twelve hemiparetic patients walked on a treadmill, and EMG, joint angle, and ultrasonography were simultaneously recorded for the gastrocnemius medialis muscle. Pennation angle was automatically extracted from ultrasonography using a tracking algorithm reported previously. The characteristics of EMG, joint angle, and pennation angle in gait cycle were calculated for both (affected and unaffected) sides of lower limbs. The results suggest that pennation angle could work as an important morphological index to continuous muscle contraction. The change pattern of pennation angle between the affected and unaffected sides is different from that of EMG. These findings indicate that morphological parameter extracted from ultrasonography can provide different information from that provided by EMG for hemiparetic gait.

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.

Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions

High-frequency ultrasound (HFU) has the ability to image both skeletal and cardiac muscles. The quantitative assessment of these myofiber orientations has a number of applications in both research and clinical examinations; however, difficulties arise due to the severe speckle noise contained in the HFU images. Thus, for the purpose of automatically measuring myofiber orientations from two-dimensional HFU images, we propose a two-step multiscale image decomposition method. It combines a nonlinear anisotropic diffusion filter and a coherence enhancing diffusion filter to extract myofibers. This method has been verified by ultrasound data from simulated phantoms, excised fiber phantoms, specimens of porcine hearts, and human skeletal muscles in vivo. The quantitative evaluations of both phantoms indicated that the myofiber measurements of our proposed method were more accurate than other methods. The myofiber orientations extracted from different layers of the porcine hearts matched the prediction of an established cardiac mode and demonstrated the feasibility of extracting cardiac myofiber orientations from HFU images ex vivo. Moreover, HFU also demonstrated the ability to measure myofiber orientations in vivo.

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.

Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients

BACKGROUND

Improvement in muscle strength is an important aim for the rehabilitation of hemiplegia patients. Presently, the rehabilitation prescription depends on the evaluation results of muscle strength, which are routinely estimated by experienced physicians and therefore not finely quantitative. Widely-used quantification methods for disability, such as Barthel Index (BI) and motor component of Functional Independent Measure (M-FIM), yet have limitations in their application, since both of them differentiated disability better in lower than higher disability, and they are subjective and recorded in wide scales. In this paper, to explore finely quantitative measures for evaluation of muscle strength level (MSL), we start with the study on quantified electromyography (EMG) and sonomyography (SMG) features of tibialis anterior (TA) muscles among hemiplegia patients.

METHODS

12 hemiplegia subjects volunteered to perform several sets of plantar-flexion movements in the study, and their EMG signals and SMG signals were recorded on TA independently to avoid interference. EMG data were filtered and then the root-mean-square (RMS) was computed. SMG signals, specifically speaking, the muscle thickness of TA, were manually measured by two experienced operators using ultrasonography. Reproducibility of the SMG assessment on TA between operators was evaluated by non-parametric test (independent sample T test). Possible relationship between muscle thickness changes (TC) of TA and muscle strength level of hemiplegia patients was estimated.

RESULTS

Mean of EMG RMS between subjects is found linearly correlated with MSL (R2 = 0.903). And mean of TA muscle TC amplitudes is also linearly correlated with MSL among dysfunctional legs (R2 = 0.949). Moreover, rectified TC amplitudes (dysfunctional leg/ healthy leg, DLHL) and rectified EMG signals (DLHL) are found in linear correlation with MSL, with R2 = 0.756 and R2 = 0.676 respectively. Meanwhile, the preliminary results demonstrate that patients' peak values of TC are generally proportional to their personal EMG peak values in 12 dysfunctional legs and 12 healthy legs (R2 = 0.521).

CONCLUSIONS

It's concluded that SMG could be a promising option to quantitatively estimate MSL for hemiplegia patients during rehabilitation besides EMG. However, after this exploratory study, they should be further investigated on a larger number of subjects.

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.

Use of optical flow to estimate continuous changes in muscle thickness from ultrasound image sequences

Muscle thickness is one of the most widely used parameters for quantifying muscle function. Ultrasonography is frequently used to estimate changes in muscle thickness in both static and dynamic contractions. Conventionally, most such measurements are conducted by manual analysis of ultrasound images. This manual approach is time consuming, subjective, susceptible to error and not suitable for measuring dynamic change. In this study, we developed an automated tracking method based on an optical flow algorithm using an affine motion model. The goal of the study was to evaluate the performance of the proposed method by comparing it with the manual approach and by determining its repeatability. Real-time B-mode ultrasound was used to examine the rectus femoris during voluntary contraction. The coefficient of multiple correlation (CMC) was used to quantify the level of agreement between the two methods and the repeatability of the proposed method. The two methods were also compared by linear regression and Bland-Altman analysis. The findings indicated that the results obtained using the proposed method were in good agreement with those obtained using the manual approach (CMC = 0.97 ± 0.02, difference = -0.06 ± 0.22 mm) and were highly repeatable (CMC = 0.91 ± 0.07). In conclusion, the automated method proposed here provides an accurate, highly repeatable and efficient approach to the estimation of muscle thickness during muscle contraction.

Towards the application of one-dimensional sonomyography for powered upper-limb prosthetic control using machine learning models

BACKGROUND

The inherent properties of surface electromyography limit its potential for multi-degrees of freedom control. Our previous studies demonstrated that wrist angle could be predicted by muscle thickness measured from B-mode ultrasound, and hence, it could be an alternative signal for prosthetic control. However, an ultrasound imaging machine is too bulky and expensive.

OBJECTIVE

We aim to utilize a portable A-mode ultrasound system to examine the feasibility of using one-dimensional sonomyography (i.e. muscle thickness signals detected by A-mode ultrasound) to predict wrist angle with three different machine learning models - (1) support vector machine (SVM), (2) radial basis function artificial neural network (RBF ANN), and (3) back-propagation artificial neural network (BP ANN).

STUDY DESIGN

Feasibility study using nine healthy subjects.

METHODS

Each subject performed wrist extension guided at 15, 22.5, and 30 cycles/minute, respectively. Data obtained from 22.5 cycles/minute trials was used to train the models and the remaining trials were used for cross-validation. Prediction accuracy was quantified by relative root mean square error (RMSE) and correlation coefficients (CC).

RESULTS

Excellent prediction was noted using SVM (RMSE = 13%, CC = 0.975), which outperformed the other methods.

CONCLUSION

It appears that one-dimensional sonomyography could be an alternative signal for prosthetic control. Clinical relevance Surface electromyography has inherent limitations that prohibit its full functional use for prosthetic control. Research that explores alternative signals to improve prosthetic control (such as the one-dimensional sonomyography signals evaluated in this study) may revolutionize powered prosthesis design and ultimately benefit amputee patients.

Imaging two-dimensional mechanical waves of skeletal muscle contraction

Skeletal muscle contraction is related to rapid mechanical shortening and thickening. Recently, specialized ultrasound systems have been applied to demonstrate and quantify transient tissue velocities and one-dimensional (1-D) propagation of mechanical waves during muscle contraction. Such waves could potentially provide novel information on musculoskeletal characteristics, function and disorders. In this work, we demonstrate two-dimensional (2-D) mechanical wave imaging following the skeletal muscle contraction. B-mode image acquisition during multiple consecutive electrostimulations, speckle-tracking and a time-stamp sorting protocol were used to obtain 1.4 kHz frame rate 2-D tissue velocity imaging of the biceps brachii muscle contraction. The results present novel information on tissue velocity profiles and mechanical wave propagation. In particular, counter-propagating compressional and shear waves in the longitudinal direction were observed in the contracting tissue (speed 2.8-4.4 m/s) and a compressional wave in the transverse direction of the non-contracting muscle tissue (1.2-1.9 m/s). In conclusion, analysing transient 2-D tissue velocity allows simultaneous assessment of both active and passive muscle tissue properties.