Spatial variation and inconsistency between estimates of onset of muscle activation from EMG and ultrasound

Observing finger movement influences the stimulus-response process of the subsequent non-aiming finger movement

AIM

The present study investigated whether observing the finger movement influences the stimulus-response process of the subsequent non-aiming finger movement.

METHODS

Participants directed their eyes to the finger. Three auditory cues with 3 s intervals were provided in each trial. The participants abducted and adducted the index finger in response to the second and third cues; the first response was considered to be the previous response and the second response was considered to be the subsequent response. The time taken for the stimulus-response process was measured via reaction time. Vision was allowed from 0 to 1 s after the start cue of the previous response, after the cue of the subsequent response, or after the cues of the previous and subsequent responses.

RESULTS

Online visual information of the stationary finger accelerated the stimulus-response process of the non-aiming finger movement. The acceleration of the stimulus-response process induced by online visual information of the stationary finger was cancelled out by the previous response information, but this cancellation is itself then eliminated by the visual information from the previous response. The visual information from the previous response decelerated the stimulus-response process of the subsequent non-aiming movement, but this deceleration was then itself cancelled out by visual information of the stationary finger immediately before the subsequent non-aiming movement.

CONCLUSION

Taken together, information regarding the previous response functions as noise interfering with the processes contributing to the subsequent non-aiming movement.

Effectiveness of warm-up and dynamic balance training in preventing anterior cruciate ligament injuries in college gymnasts: a 3-year prospective study for one team

BACKGROUND

The effect of trunk stability and dynamic balance warm-up exercises on physical functional improvement remains unelucidated. This study examined whether exercises could prevent anterior cruciate ligament (ACL) injury and improve trunk muscle activation and dynamic balance in gymnasts.

METHODS

This comparison study, involving gymnastics practice sessions, included 31 university gymnasts and was conducted in two periods: 1 year of observation followed by 2 years of intervention. Participants performed a trunk and dynamic balance warm-up exercise program during the intervention. The effect of exercise on the incidence of ACL injury was evaluated. In addition, the paired t-test was used to compare the Y-balance distance and the changes in muscle thickness associated with trunk muscle activation at rest and during plank.

RESULTS

ACL injury risk during the intervention was significantly lower, with a relative risk of 0.23 (P=0.02, 95% CI: 0.06-0.88). Changes in muscle thickness with activation of the transversus abdominis (P<0.01, mean difference 4.1, 95% CI: 9.97-28.07, Cohen's d=0.52), internal oblique (P<0.01, mean difference 5.2, 95% CI: 9.72-21.55, Cohen's d=0.65), and external oblique (P<0.01, mean difference 5.5, 95% CI: 20.44-39.09, Cohen's d=0.71) muscles were significantly higher during the intervention. The Y-balance distance was also significantly greater in the posterior medial reach (P<0.01, mean difference 3.3, 95% CI: 1.56-6.26, Cohen's d=0.46) during the intervention.

CONCLUSIONS

Exercise-based warm-up programs may decrease ACL injuries. It can improve physical functions, such as the rate of change in trunk muscle thickness and the posterior medial distance during Y balance.

Individual Muscle Force Estimation in the Human Forearm Using Multi-Muscle MR Elastography (MM-MRE)

OBJECTIVE

To establish the sensitivity of magnetic resonance elastography (MRE) to active muscle contraction in multiple muscles of the forearm.

METHODS

We combined MRE of forearm muscles with an MRI-compatible device, the MREbot, to simultaneously measure the mechanical properties of tissues in the forearm and the torque applied by the wrist joint during isometric tasks. We measured shear wave speed of thirteen forearm muscles via MRE in a series of contractile states and wrist postures and fit these outputs to a force estimation algorithm based on a musculoskeletal model.

RESULTS

Shear wave speed changed significantly upon several factors, including whether the muscle was recruited as an agonist or antagonist (p = 0.0019), torque amplitude (p = <0.0001), and wrist posture (p = 0.0002). Shear wave speed increased significantly during both agonist (p = <0.0001) and antagonist (p = 0.0448) contraction. Additionally, there was a greater increase in shear wave speed at greater levels of loading. The variations due to these factors indicate the sensitivity to functional loading of muscle. Under the assumption of a quadratic relationship between shear wave speed and muscle force, MRE measurements accounted for an average of 70% of the variance in the measured joint torque.

CONCLUSION

This study shows the ability of MM-MRE to capture variations in individual muscle shear wave speed due to muscle activation and presents a method to estimate individual muscle force through MM-MRE derived measurements of shear wave speed.

SIGNIFICANCE

MM-MRE could be used to establish normal and abnormal muscle co-contraction patterns in muscles of the forearm controlling hand and wrist function.

The Sensitivity of Bipolar Electromyograms to Muscle Excitation Scales With the Inter-Electrode Distance

The value of surface electromyograms (EMGs) lies in their potential to non-invasively probe the neuromuscular system. Whether muscle excitation may be accurately inferred from bipolar EMGs depends on how much the detected signal is both sensitive and specific to the excitation of the target muscle. While both are known to be a function of the inter-electrode distance (IED), specificity has been of long concern in the physiological literature. In contrast, sensitivity, at best, has been implicitly assumed. Here we provide evidence that the IED imposes a biophysical constraint on the sensitivity of surface EMG. From 20 healthy subjects, we tested the hypothesis that excessively reducing the IED limits EMGs' physiological content. We detected bipolar EMGs with IEDs varying from 5 mm to 50 mm from two skeletal muscles with distinct architectures, gastrocnemius and biceps brachii. Non-parametric statistics and Bayesian hierarchical modelling were used to evaluate the dependence of the onset of muscle excitation and signal-to-noise ratio (SNR) on the IED. Experimental results revealed that IED critically affects the sensitivity of bipolar EMGs for both muscles-indeliberately reducing the IED yields EMGs that are not representative of the whole muscle, hampering validity. Simulation results substantiate the generalization of experimental results to small and large electrodes. Based on current and previous findings, we discuss a potentially valid procedure for defining the most appropriate IED for a single bipolar, surface recording-i.e., the distance from the electrode to the target muscle boundary may heuristically serve as a lower bound when choosing an IED.

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

BACKGROUND

Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data.

METHODS

A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles.

RESULTS

Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results.

CONCLUSIONS

This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

High-Performance Surface Electromyography Armband Design for Gesture Recognition

Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person's intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1-20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time-frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence", and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

Responsiveness of electromyographically assessed skeletal muscle inactivity: methodological exploration and implications for health benefits

Prolonged sedentary behaviour is detrimental to health due to low contractile activity in large lower extremity muscle groups. This muscle inactivity can be measured with electromyography (EMG), but it is unknown how methodological factors affect responsiveness longitudinally. This study ranks 16 different EMG inactivity thresholds based on their responsiveness (absolute and standardized effect size, responsiveness) using data from a randomized controlled trial targeted at reducing and breaking up sedentary time (InPact, ISRCTN28668090). EMG inactivity duration and usual EMG inactivity bout duration (weighted median of bout lengths) were measured from large lower extremity muscle groups (quadriceps, hamstring) with EMG-sensing shorts. The results showed that the EMG inactivity threshold above signal baseline (3 μV) provided overall the best responsiveness indices. At baseline, EMG inactivity duration of 66.8 ± 9.6% was accumulated through 73.9 ± 36.0 s usual EMG inactivity bout duration, both of which were reduced following the intervention (-4.8 percentage points, -34.3 s). The proposed methodology can reduce variability in longitudinal designs and the detailed results can be used for sample size calculations. Reducing EMG inactivity duration and accumulating EMG inactivity in shorter bouts has a potential influence on muscle physiology and health.

Differences between vastus medialis and lateralis excitation onsets are dependent on the relative distance of surface electrodes placement from the innervation zone location

Conflictual results between the onset of vastus medialis (VM) and vastus lateralis (VL) excitation may arise from methodological aspects related to the detection of surface electromyograms. In this study we used an array of surface electrodes to assess the effect of detection site, relative to the muscle innervation zone, on the difference between VM and VL excitation onsets. Ten healthy males performed moderate isometric knee extension at 40 % of their maximal voluntary isometric contraction. After the actual VM-VL onset was defined (estimated when action potentials were generated at the neuromuscular junctions of both muscles), we calculated the largest bias that the detection site may introduce in the VM-VL onset estimation. We also assessed whether the location often considered for positioning bipolar electrodes on each muscle leads to VM-VL onset estimations comparable to the actual VM-VL onset. Our main results revealed that a maximum absolute bias of 20.48 ms may be introduced in VM-VL onset estimations due to the electrodes' detection site. In addition, mean differences of ∼ 12 ms in VM-VL onset estimations were attributable to largest possible discrepancies in the paired position of channels with respect to the innervation zone for VL and VM. When considering the classical location for positioning the bipolar electrodes over these muscles, differences error was subtle (∼3.4 ms) when compared with the actual VM-VL onset. Nonetheless, when accounting for the effect of relative differences in electrode position between muscles is not possible, our results suggest that a systematic absolute error of ∼ 12 ms should be considered in future studies regarding VM-VL onset estimations, suggesting that onset differences lower than that might not be clinically relevant.

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

OBJECTIVE

The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e., the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions.

APPROACH

We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation-contraction (E-C) coupling delay.

MAIN RESULTS

Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E-C coupling were 17.9 ± 5.3 ms, 56.6 ± 8.4 ms, and 3.8 ± 3.0 ms (n = 390 across 10 participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions.

SIGNIFICANCE

The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions.

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).

Application of Surface Electromyography in Exercise Fatigue: A Review

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

Toward a wearable monitor of local muscle fatigue during electrical muscle stimulation using tissue Doppler imaging

Electrical muscle stimulation (EMS) is widely used in rehabilitation and athletic training to generate involuntary muscle contractions. However, EMS leads to rapid muscle fatigue, limiting the force a muscle can produce during prolonged use. Currently available methods to monitor localized muscle fatigue and recovery are generally not compatible with EMS. The purpose of this study was to examine whether Doppler ultrasound imaging can assess changes in stimulated muscle twitches that are related to muscle fatigue from electrical stimulation. We stimulated five isometric muscle twitches in the medial and lateral gastrocnemius of 13 healthy subjects before and after a fatiguing EMS protocol. Tissue Doppler imaging of the medial gastrocnemius recorded muscle tissue velocities during each twitch. Features of the average muscle tissue velocity waveforms changed immediately after the fatiguing stimulation protocol (peak velocity: -38%, p = .022; time-to-zero velocity: +8%, p = .050). As the fatigued muscle recovered, the features of the average tissue velocity waveforms showed a return towards their baseline values similar to that of the normalized ankle torque. We also found that features of the average tissue velocity waveform could significantly predict the ankle twitch torque for each participant (R2 = 0.255-0.849, p < .001). Our results provide evidence that Doppler ultrasound imaging can detect changes in muscle tissue during isometric muscle twitch that are related to muscle fatigue, fatigue recovery, and the generated joint torque. Tissue Doppler imaging may be a feasible method to monitor localized muscle fatigue during EMS in a wearable device.

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

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.

[Feeling stiff…but what does it mean objectively? : Can you measure muscle tension?]

Almost everyone is familiar with "tense muscles", but what is muscle tension physiologically behind? Are tense muscles more active; do they have problems relaxing? Are they harder or stiffer than asymptomatic muscles? In this work, current evidence regarding the activity and stiffness of tense neck muscles is presented. Further, measurement methods and their limitations are explained. These limitations reveal the shortcomings of the current knowledge and the need for further research. Finally, a recently funded research project on the measurement of tense muscles is presented.

Preliminary Validation of Upper Limb Musculoskeletal Model using Static Optimization

Musculoskeletal models are powerful analogues to simulate human motion through kinematic and dynamic analysis. When coupled with feature-rich software, musculoskeletal models form an attractive platform for the integration of machine learning for human motion analysis. Performing realistic simulations using these models provide an avenue to overcome constraints when collecting real-world data sets. This motivates the need to further investigate the validity, efficacy, and accuracy of each available model to ensure that the resultant simulations are transferable to real-world applications. Using the open-source software, OpenSim, the primary aim of this paper is to validate an upper limb musculoskeletal model widely used in research. Muscle activation results from static optimization are evaluated against real-world data. A secondary aim is to investigate the effects of two muscle force generation constraints when evaluating the model's validity. Results show an agreement between the optimized muscle activation trends and real-world sEMG readings. However, it was found that static optimization of the musculoskeletal model is unable to identify voluntary co-contractions since the redundant model has more muscles than the system's degrees of freedom. Thus, future work will look to utilize additional channels of information to incorporate this during analysis.

Influence of the Inter-Trial Interval, Movement Observation, and Hand Dominance on the Previous Trial Effect

Previous studies have shown that current movement is influenced by the previous movement, which is known as the previous trial effect. In this study, we investigated the influence of the inter-trial interval, movement observation, and hand dominance on the previous trial effect of the non-target discrete movement. Right-handed healthy humans abducted the index finger in response to a start cue, and this task was repeated with constant inter-trial intervals. The absolute difference in the reaction time (RT) between the previous and current trials increased as the inter-trial interval increased. The absolute difference in RT reflects the reproducibility of the time taken for the motor execution between two consecutive trials. Thus, the finding supported the view that there is a carryover of movement information from one trial to the next, and that the underlying reproducibility of the RT between the two consecutive trials decays over time. This carryover of movement information is presumably conveyed by implicit short-term memory, which also decays within a short period of time. The correlation coefficient of the RT between the previous and current trials decreased with an increase in the inter-trial interval, indicating that the common responsiveness of two consecutive trials weakens over time. The absolute difference was smaller when the response was performed while observing finger movement, indicating that a carryover of the visual information to the next trial enhances the reproducibility of the motor execution process between consecutive trials. Hand dominance did not influence the absolute difference or correlation coefficient, indicating that the central process mediating previous trial effect of hand movement is not greatly lateralized.

Modulation of bilateral lower-limb muscle coordination when performing increasingly challenging balance exercises

Balance exercises have proven effective in enhancing and regaining neuromuscular control. However, how the bilateral homonymous muscles are coordinated to achieve bipedal equilibrium remains unclear. In terms of increasingly difficult balance tasks, the current study focused on two levels of muscle coordination: individual homonymous muscles and groups of homonymous muscles. In 25 physically active young adults, a cross-correlation between the bilateral electromyographic (EMG) signals of both legs (i.e., bilateral EMG-EMG correlation) was conducted on seven muscles measured when performing bipedal balancing on three different support surface instabilities. Then, the patterns of bilateral EMG-EMG cross-correlation coefficients were determined through a principal component analysis (PCA). It was hypothesized that modulations of bilateral lower-limb muscle coordination should be observed in the specific relevant muscles or in the patterns of bilateral muscle coordination. The results showed that only the first hypothesis was supported as changes in the strength of bilateral EMG-EMG correlation (p ≤ 0.005) and in the time delays (p < 0.001) were mostly restricted in the lower-leg muscles. The dorsiflexor and plantar flexor muscles showed opposite coordination behaviors. Larger bilateral EMG-EMG correlation and shorter time delays appeared only in the tibialis anterior muscle, suggesting that bilateral dorsiflexor muscle coordination is needed for exercising on multiaxial-unstable platforms.

Motion blur invariant for estimating motion parameters of medical ultrasound images

High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.

Common Motor Drive Triggers Response of Prime Movers When Two Fingers Simultaneously Respond to a Cue

This study investigated whether the motor execution process of one finger movement in response to a start cue is influenced by the participation of another finger movement and whether the process of the finger movement is dependent on the movement direction. The participants performed a simple reaction time (RT) task, the abduction or flexion of one (index or little finger) or two fingers (index and little fingers). The RT of the prime mover for the finger abduction was significantly longer than that for the flexion, indicating that the time taken for the motor execution of the finger response is dependent on the movement direction. The RT of the prime mover was prolonged when the abduction of another finger, whose RT was longer than the flexion, was added. This caused closer RTs between the prime movers for a two-finger response compared with the RTs for a one finger response. The absolute difference in the RT between the index and little finger responses became smaller when two fingers responded together compared with one finger response. Those results are well explained by a view that the common motor drive triggers the prime movers when two fingers move together in response to a start cue.

Online Adaptive Prediction of Human Motion Intention Based on sEMG

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

Principal postural acceleration and myoelectric activity: Interrelationship and relevance for characterizing neuromuscular function in postural control

One approach to investigating sensorimotor control is to assess the accelerations that produce changes in the kinematic state of the system. When assessing complex whole-body movements, structuring the multi-segmental accelerations is important. A useful structuring can be achieved through a principal component analysis (PCA) performed on segment positions followed by double-differentiation to obtain "principal accelerations" (PAs). In past research PAs have proven sensitive to altered motor control strategies, however, the interrelationship between PAs and muscle activation (surface electromyography, sEMG) have never been determined. The purpose of the current study was therefore to assess the relationship between PAs and sEMG signals recorded from muscles controlling the ankle joint during one-leg standing trials. It was hypothesized that medium correlation should be observed when accounting for neurophysiologic latencies (electro-mechanical delay). Unipedal balancing on a level-rigid ground was performed by 25 volunteers. sEMG activities were recorded from the tibialis anterior, peroneus longus, gastrocnemius medialis, and soleus muscles of the stance leg. The first eight PA-time series were determined from kinematic marker data. Then, a cross-correlation analysis was performed between sEMG and PA time series. We found that peak correlation coefficients for many participants aligned at time delays between 0.116 and 0.362 s and were typically in the range small to medium (|r| = 0.1 to 0.6). Thus, the current study confirmed a direct association between many principal accelerations PA(t) and muscle activation signals recorded from four muscles crossing the ankle joint complex. The combined analysis of PA and sEMG signals allowed exploring the neuromuscular function of each muscle in different postural movement components.

Ultrasound Sensing Can Improve Continuous Classification of Discrete Ambulation Modes Compared to Surface Electromyography

Clinical translation of "intelligent" lower-limb assistive technologies relies on robust control interfaces capable of accurately detecting user intent. To date, mechanical sensors and surface electromyography (EMG) have been the primary sensing modalities used to classify ambulation. Ultrasound (US) imaging can be used to detect user-intent by characterizing structural changes of muscle. Our study evaluates wearable US imaging as a new sensing modality for continuous classification of five discrete ambulation modes: level, incline, decline, stair ascent, and stair descent ambulation, and benchmarks performance relative to EMG sensing. Ten able-bodied subjects were equipped with a wearable US scanner and eight unilateral EMG sensors. Time-intensity features were recorded from US images of three thigh muscles. Features from sliding windows of EMG signals were analyzed in two configurations: one including 5 EMG sensors on muscles around the thigh, and another with 3 additional sensors placed on the shank. Linear discriminate analysis was implemented to continuously classify these phase-dependent features of each sensing modality as one of five ambulation modes. US-based sensing statistically improved mean classification accuracy to 99.8% (99.5-100% CI) compared to 8-EMG sensors (85.8%; 84.0-87.6% CI) and 5-EMG sensors (75.3%; 74.5-76.1% CI). Further, separability analyses show the importance of superficial and deep US information for stair classification relative to other modes. These results are the first to demonstrate the ability of US-based sensing to classify discrete ambulation modes, highlighting the potential for improved assistive device control using less widespread, less superficial and higher resolution sensing of skeletal muscle.

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.

Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion

Seamless integration of lower-limb assistive devices with the human body requires an intuitive human-machine interface, which would benefit from predicting the intent of individuals in advance of the upcoming motion. Ultrasound imaging was recently introduced as an intuitive sensing interface. The objective of the present study was to investigate the predictability of joint kinematics using ultrasound features of the rectus femoris muscle during a non-weight-bearing knee extension/flexion. Motion prediction accuracy was evaluated in 67 ms increments, up to 600 ms in time. Statistical analysis was used to evaluate the feasibility of motion prediction, and the linear mixed-effects model was used to determine a prediction time window where the joint angle prediction error is barely perceivable by the sample population, hence clinically reliable. Surprisingly, statistical tests revealed that the prediction accuracy of the joint angle was more sensitive to temporal shifts than the accuracy of the joint angular velocity prediction. Overall, predictability of the upcoming joint kinematics using ultrasound features of skeletal muscle was confirmed, and a time window for a statistically and clinically reliable prediction was found between 133 and 142 ms. A reliable prediction of user intent may provide the time needed for processing, control planning, and actuation of the assistive devices at critical points during ambulation, contributing to the intuitive behavior of lower-limb assistive devices.

Skeletal Muscle Extracellular Matrix - What Do We Know About Its Composition, Regulation, and Physiological Roles? A Narrative Review

Skeletal muscle represents the largest body-composition component in humans. In addition to its primary function in the maintenance of upright posture and the production of movement, it also plays important roles in many other physiological processes, including thermogenesis, metabolism and the secretion of peptides for communication with other tissues. Research attempting to unveil these processes has traditionally focused on muscle fibers, i.e., the contractile muscle cells. However, it is a frequently overlooked fact that muscle fibers reside in a three-dimensional scaffolding that consists of various collagens, glycoproteins, proteoglycans, and elastin, and is commonly referred to as extracellular matrix (ECM). While initially believed to be relatively inert, current research reveals the involvement of ECM cells in numerous important physiological processes. In interaction with other cells, such as fibroblasts or cells of the immune system, the ECM regulates muscle development, growth and repair and is essential for effective muscle contraction and force transmission. Since muscle ECM is highly malleable, its texture and, consequently, physiological roles may be affected by physical training and disuse, aging or various diseases, such as diabetes. With the aim to stimulate increased efforts to study this still poorly understood tissue, this narrative review summarizes the current body of knowledge on (i) the composition and structure of the ECM, (ii) molecular pathways involved in ECM remodeling, (iii) the physiological roles of muscle ECM, (iv) dysregulations of ECM with aging and disease as well as (v) the adaptations of muscle ECM to training and disuse.

Prediction of Distal Lower-Limb Motion Using Ultrasound-Derived Features of Proximal Skeletal Muscle

Control of lower-limb assistive devices would benefit from predicting the intent of individuals in advance of upcoming motion, rather than estimating the current states of their motion. Human lower-limb motion estimation using ultrasound (US) image derived features of skeletal muscle has been demonstrated. However, predictability of motion in time remains an open question. The objective of this study was to assess the predictability of distal lower-limb motion using US image features of rectus femoris (RF) muscle during non-weight-bearing knee flexion/extension. A series of time shifts was introduced between the US features and the joint position in 67 ms steps from 0 ms (i.e., estimation, no prediction) up to predicting 467 ms in advance. A US-based algorithm to estimate lower-limb motion was then used to predict the knee joint position in time using the US features after introducing the time shifts. The accuracy of joint motion prediction after each time shift was compared to the accuracy of joint motion estimation. The reliability of the prediction was then assessed using an analysis of variance (ANOVA) test. The motion prediction accuracy was found to be reliable up to 200 ms, where the average root mean square error (RMSE) of prediction across 9 healthy subjects was 0.89 degrees greater than the average RMSE (7.39 degrees) of motion estimation for the same group of subjects. These findings suggest a reliable prediction of upcoming lower-limb motion is feasible using the US features of skeletal muscle up to a certain point. A reliable prediction may provide lower-limb assistive device control systems with a time-window for processing and control planning, and actuation hence improving the volitional control behaviors of lower-limb assistive devices.

Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control

OBJECTIVE

Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements.

METHODS

A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity.

RESULTS

The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45° and 0.262 rad/s, respectively. The average processing rate was 3-6 frames/s that is promising toward real-time implementation.

CONCLUSION

Experimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications.

SIGNIFICANCE

Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.

Differences in muscle contraction onset as determined by ultrasound and electromyography

INTRODUCTION

We characterize the agreement between the timing of muscle contraction onset detected by surface electromyography (sEMG), fine wire EMG (fwEMG), and motion-mode (M-mode) ultrasound for improved interpretations of clinical outcomes.

METHODS

Eighteen healthy adults participated. Differences in contraction onset were compared between sEMG, fwEMG, and M-mode ultrasound collected during concentric contractions of the vastus lateralis and biceps brachii.

RESULTS

The mean difference of 13.1 ms (-33.3-59.9) between sEMG and fwEMG was non-significant (intraclass correlation [ICC] = 0.60). Ultrasound was significantly different from surface and fine wire EMG (ICC = 0.65 and ICC = 0.40, respectively), occurring 98.6 ms (72.3-124.9) and 111.7 (60.3-163.0) before sEMG and fwEMG, respectively. Nonparametric interquartile ranges were also wide.

CONCLUSIONS

Due to high variability, comparisons between EMG methods should be interpreted with caution. Ultrasound detected onset before either EMG method, which may indicate motion from adjacent muscles during voluntary contractions. Muscle Nerve 59:494-500, 2019.

Surface electromyographic analysis of differential effects in kettlebell carries for the serratus anterior muscles

The purpose of this study was to examine differences in the Electromyography (EMG) amplitude of the serratus anterior between 45° kettlebell carries and 90° kettlebell carries. Thirty-three men aged roughly between 19 and 23 and who were either college or professional baseball pitchers were chosen and randomly assigned to either perform the 45° kettlebell carry followed by the 90° kettlebell carry (n = 17) or the 90° kettlebell carry followed by the 45° kettlebell carry (n = 16). Each pitcher was instructed in the proper usage of the exercise and assigned a short break between the two carries. Changes in EMG amplitude were examined after proper band-pass filtering, normalization, and moving average-smoothing of the raw EMG signal. Differences of the EMG amplitude mean frequencies were examined between each subject’s individual carries and the clumped groups of all 45° and 90° carries. Among each individual comparison, eight pitchers had “large” Effect Size differences between the EMG amplitudes of their two carries, with seven of them signaling the 45° carry as the larger value. In addition, when examining the grouped mean differences of the EMG amplitudes, we found the 45° carries to be significantly higher (p-value of 0.018).

Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences

Surface electromyography (sEMG) is a popular research tool in sport and rehabilitation sciences. Common study designs include the comparison of sEMG amplitudes collected from different muscles as participants perform various exercises and techniques under different loads. Based on such comparisons, researchers attempt to draw conclusions concerning the neuro- and electrophysiological underpinning of force production and hypothesize about possible longitudinal adaptations, such as strength and hypertrophy. However, such conclusions are frequently unsubstantiated and unwarranted. Hence, the goal of this review is to discuss what can and cannot be inferred from comparative research designs as it pertains to both the acute and longitudinal outcomes. General methodological recommendations are made, gaps in the literature are identified, and lines for future research to help improve the applicability of sEMG are suggested.

Review of devices used in neuromuscular electrical stimulation for stroke rehabilitation

Neuromuscular electrical stimulation (NMES), specifically functional electrical stimulation (FES) that compensates for voluntary motion, and therapeutic electrical stimulation (TES) aimed at muscle strengthening and recovery from paralysis are widely used in stroke rehabilitation. The electrical stimulation of muscle contraction should be synchronized with intended motion to restore paralysis. Therefore, NMES devices, which monitor electromyogram (EMG) or electroencephalogram (EEG) changes with motor intention and use them as a trigger, have been developed. Devices that modify the current intensity of NMES, based on EMG or EEG, have also been proposed. Given the diversity in devices and stimulation methods of NMES, the aim of the current review was to introduce some commercial FES and TES devices and application methods, which depend on the condition of the patient with stroke, including the degree of paralysis.