An examination of cross-talk among surface mechanomyographic signals from the superficial quadriceps femoris muscles during isometric muscle actions

BiLSTM-Based Joint Torque Prediction From Mechanomyogram During Isometric Contractions: A Proof of Concept Study

Quantifying muscle strength is an important measure in clinical settings; however, there is a lack of practical tools that can be deployed for routine assessment. The purpose of this study is to propose a deep learning model for ankle plantar flexion torque prediction from time-series mechanomyogram (MMG) signals recorded during isometric contractions (i.e., a similar form to manual muscle testing procedure in clinical practice) and to evaluate its performance. Four different deep learning models in terms of model architecture (based on a stacked bidirectional long short-term memory and dense layers) were designed with different combinations of the number of units (from 32 to 512) and dropout ratio (from 0.0 to 0.8), and then evaluated for prediction performance by conducting the leave-one-subject-out cross-validation method from the 10-subject dataset. As a result, the models explained more variance in the untrained test dataset as the error metrics (e.g., root-mean-square error) decreased and as the slope of the relationship between the measured and predicted joint torques became closer to 1.0. Although the slope estimates appear to be sensitive to an individual dataset, >70% of the variance in nine out of 10 datasets was explained by the optimal model. These results demonstrated the feasibility of the proposed model as a potential tool to quantify average joint torque during a sustained isometric contraction.

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Is the Interpolated-Twitch Technique-Derived Voluntary Activation Just Neural? Novel Perspectives from Mechanomyographic Data

PURPOSE

Voluntary activation (VA) determined by interpolation-twitch technique could be affected by the characteristics of the in-series elastic components. To overcome this possible bias, a novel approach based on the mechanomyographic (MMG) signal to detect voluntary activation (VA MMG ) has been proposed. We examined the changes in VA and VA MMG after passive stretching to check the influence of neural and mechanical factors in the force output.

METHODS

Twenty-six healthy men underwent VA assessment using the interpolated-twitch technique before and after unilateral passive stretching of the plantarflexors (five 45-s on + 15-s off). In addition to the force signal, the MMG signal was detected on gastrocnemius medialis, gastrocnemius lateralis, and soleus. From the force and MMG signal analysis, VA and VA MMG were calculated in the stretched and contralateral nonstretched limbs. Joint passive stiffness was also defined.

RESULTS

In the stretched limb, passive stretching increased dorsiflexion range (mean ± SD = +18% ± 10%, P < 0.001, ES = 1.54) but reduced joint passive stiffness (-22% ± 8%, P < 0.001, ES = -1.75), maximum voluntary contraction (-15% ± 7%, P < 0.001, ES = -0.87), VA (-7% ± 3%, P < 0.001, ES = -2.32), and VA MMG (~-5% ± 2%, P < 0.001, ES = -1.26/-1.14). In the contralateral nonstretched limb, passive stretching increased dorsiflexion range (+10% ± 6%, P < 0.001, ES = 0.80) but reduced joint passive stiffness (-3% ± 2%, P = 0.041, ES = -0.27), maximum voluntary contraction (-4% ± 3%, P = 0.035, ES = -0.24), VA (-4% ± 2%, P < 0.001, ES = -1.77), and VA MMG (~- 2% ± 1%, P < 0.05, ES = -0.54/-0.46). The stretch-induced changes in VA correlated with VA MMG ( R ranging from 0.447 to 0.583 considering all muscles) and with joint passive stiffness (stretched limb: R = 0.503; contralateral nonstretched limb: R = 0.530).

CONCLUSIONS

VA output is overall influenced by both neural and mechanical factors, not distinguishable using the interpolated-twitch technique. VA MMG is a complementary index to assess the changes in VA not influenced by mechanical factors and to examine synergistic muscles.

A noninvasive test for estimating myosin heavy chain of the vastus lateralis in females with mechanomyography

This study examined relationships between percent myosin heavy chain (%MHC) expression and mechanomyographic amplitude (MMG_RMS). Fifteen females (age ± SD=21.3 ± 5.3 yrs) completed isometric trapezoidal contractions at 30% and 70% maximal voluntary contraction (MVC). MMG was recorded from the vastus lateralis (VL). Participants gave a muscle biopsy of the VL post-testing. MMG_RMS-torque relationships during the linearly varying segments were log-transformed and linear regressions were applied to calculate b terms (slopes). For the steady torque segment, MMG_RMS was averaged. Correlations were performed for type I%MHC with the MMG variables. Multiple regression was utilized to examine prediction equations for type I%MHC. Type I%MHC was significantly correlated with the b terms during the increasing segment of the 70% MVC (p = 0.003; r = -0.718), and MMG_RMS during steady torque at 30% (p = 0.008; r = -0.652) and 70% MVC (p = 0.040; r = -0.535). Type I%MHC reduced the linearity of the MMG_RMS-torque relationship during the high-intensity linearly increasing segment, and MMG_RMS at a low- and high-intensity steady torque. A combination of MMG variables estimated type I%MHC expression with 81.2% accuracy. MMG recorded during a low- and high-intensity isometric trapezoidal contraction may offer a simple, noninvasive test for estimating type I%MHC expression of the VL in sedentary females.

Analysis of anthropometrics and mechanomyography signals as forearm flexion, pronation and supination torque predictors

This study aimed to analyze anthropometrics and mechanomyography (MMG) signals as forearm flexion, pronation, and supination torque predictors. 25 young, healthy, male participants performed isometric forearm flexion, pronation, and supination tasks from 20 to 100% maximal voluntary isometric contraction (MVIC) while maintaining 90° at the elbow joint. Nine anthropometric measures were recorded, and MMG signals from the biceps brachii (BB), brachialis (BRA), and brachioradialis (BRD) muscles were digitally acquired using triaxial accelerometers. These were then correlated with torque values. Significant positive correlations were found for arm circumference (CA) and MMG root mean square (RMS) values with flexion torque. Flexion torque might be predicted using CA (r = 0.426–0.575), a pseudo for muscle size while MMG_RMS (r = 0.441), an indication of muscle activation.

Determining voluntary activation in synergistic muscles: a novel mechanomyographic approach

PURPOSE

Drawing on correlations between the mechanomyographic (MMG) and the force signal, we devised a novel approach based on MMG signal analysis to detect voluntary activation (VA) of the synergistic superficial heads of the quadriceps muscle. We hypothesized that, after a fatiguing exercise, the changes in the evoked MMG signal of each quadriceps head would correlate with the changes in the level of VA in the whole quadriceps.

METHODS

Twenty-five men underwent a unilateral single-leg quadriceps exercise to failure. Before and after exercise, VA was assessed by interpolated-twitch-technique via nerve stimulation during and after maximum voluntary contraction (MVC). The force and MMG signal were recorded from vastus lateralis, vastus medialis, and rectus femoris. The MMG peak-to-peak was calculated and the voluntary activation index (VA_MMG), defined as the superimposed/potentiated MMG peak-to-peak ratio, was determined from the MMG signal for each head.

RESULTS

VA_MMG presented a very high intraclass correlation coefficient (0.981-0.998) and sensitivity (MDC_95%: 0.42-6.97%). MVC and VA were decreased after exercise in both the exercising [MVC:-17(5)%, ES -0.92; VA: -7(3)%, ES -1.90] and the contralateral limb [MVC: -9(4)%, ES -0.48; VA: -4(1)%, ES -1.51]. VA_MMG was decreased in both the exercising [~ -9(6)%, ES -1.77] and contralateral limb [~ -3(2)%, ES -0.57], with a greater decrease in VA_MMG noted only in the vastus medialis of the exercising limb. Moderate-to-very high correlations were found between VA_MMG and VA (R-range: 0.503-0.886) before and after exercise.

CONCLUSION

VA_MMG may be implemented to assess VA and provide further information when multiple synergistic muscle heads are involved in fatiguing exercises.

Mechanomyogram amplitude vs. isometric ankle plantarflexion torque of human medial gastrocnemius muscle at different ankle joint angles

The purpose of this study was to investigate the influence of changes in ankle joint angle on the mechanomyogram (MMG) amplitude of the human medial gastrocnemius (MG) muscle during voluntary isometric plantarflexion contractions. Ten healthy individuals were asked to perform voluntary isometric contractions at six different contraction intensities (from 10% to 100%) and at three different ankle joint angles (plantarflexion of 26°; plantarflexion of 10°; dorsiflexion of 3°). MMG signals were recorded from the surface over the MG muscle, using a 3-axis accelerometer. The relations between root mean square (RMS) MMG and isometric plantarflexion torque at different ankle joint angles were characterized to evaluate the effects of altered muscle mechanical properties on RMS MMG. We found that the relation between RMS MMG and plantarflexion torque is changed at different ankle joint angles: RMS MMG increases monotonically with increasing the plantarflexion torque but decreases as the ankle joint became dorsiflexed. Moreover, RMS MMG shows a negative correlation with muscle length, with passive torque, and with maximum voluntary torque, which were all changed significantly at different ankle joint angles. Our findings demonstrate the potential effects of changing muscle mechanical properties on muscle vibration amplitude. Future studies are required to explore the major sources of this muscle vibration from the perspective of muscle mechanics and muscle activation level, attributable to changes in the neural command.

Crosstalk in Mechanomyographic Signals From Elbow Flexor Muscles During Submaximal to Maximal Isometric Flexion, Pronation, and Supination Torque Tasks

This study analyzed the crosstalk in mechanomyographic (MMG) signals from elbow flexors during isometric muscle actions from 20% to 100% maximum voluntary isometric contraction (MVIC). Twenty-five young, healthy, male participants performed the isometric elbow flexion, forearm pronation, and supination tasks at an elbow joint angle of 90 deg. The MMG signals from the biceps brachii (BB), brachialis (BRA), and brachioradialis (BRD) muscles were recorded using accelerometers. The cross-correlation coefficient was used to quantify the crosstalk in MMG signals, recorded in a direction transverse to muscle fiber axis, among the muscle pairs (P1: BB and BRA, P2: BRA and BRD, and P3: BB and BRD). In addition, the MMG RMS and MPF were quantified. The mean normalized RMS and mean MPF exhibited increasing (r > 0.900) and decreasing (r < -0.900) trends, respectively, with increases in the effort levels in all three tasks. The magnitude of crosstalk ranged from 0.915% to 21.565% in all three muscle pairs. The crosstalk was found to exhibit high positive correlations with submaximal to maximal flexion [P1 (r = 0.970), P2 (r = 0.951), and P3 (r = 0.824)], pronation [P1 (r = 0.811), P2 (r = 0.763), and P3 (r = 0.901)] and supination [P1 (r = 0.898), P2 (r = 0.838), and P3 (r = 0.852)] torque levels (eight out of nine p-values were < 0.05). Regardless of the high positive correlation between crosstalk and level of effort, the crosstalk remained at a low range (0.915-21.565%) with increases in the torque levels.

Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks

This study aimed to quantify the association of four anthropometric parameters of the human arm, namely, the arm circumference (CA), arm length (LA), skinfold thickness (ST) and inter-sensor distance (ISD), with amplitude (RMS) and crosstalk (CT) of mechanomyography (MMG) signals. Twenty-five young, healthy, male participants were recruited to perform forearm flexion, pronation and supination torque tasks. Three accelerometers were employed to record the MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) at 80% maximal voluntary contraction (MVC). Signal RMS was used to quantify the amplitude of the MMG signals from a muscle, and cross-correlation coefficients were used to quantify the magnitude of the CT among muscle pairs (BB & BRA, BRA & BRD, and BB & BRD). For all investigated muscles and pairs, RMS and CT showed negligible to low negative correlations with CA, LA and ISD (r = −0.0001–−0.4611), and negligible to moderate positive correlations with ST (r = 0.004–0.511). However, almost all of these correlations were statistically insignificant (p > 0.05). These findings suggest that RMS and CT values for the elbow flexor muscles recorded and quantified using accelerometers appear invariant to anthropometric parameters.

A review on crosstalk in myographic signals

PURPOSE

Crosstalk in myographic signals is a major hindrance to the understanding of local information related to individual muscle function. This review aims to analyse the problem of crosstalk in electromyography and mechanomyography.

METHODS

An initial search of the SCOPUS database using an appropriate set of keywords yielded 290 studies, and 59 potential studies were selected after all the records were screened using the eligibility criteria. This review on crosstalk revealed that signal contamination due to crosstalk remains a major challenge in the application of surface myography techniques. Various methods have been employed in previous studies to identify, quantify and reduce crosstalk in surface myographic signals.

RESULTS

Although correlation-based methods for crosstalk quantification are easy to use, there is a possibility that co-contraction could be interpreted as crosstalk. High-definition EMG has emerged as a new technique that has been successfully applied to reduce crosstalk.

CONCLUSIONS

The phenomenon of crosstalk needs to be investigated carefully because it depends on many factors related to muscle task and physiology. This review article not only provides a good summary of the literature on crosstalk in myographic signals but also discusses new directions related to techniques for crosstalk identification, quantification and reduction. The review also provides insights into muscle-related issues that impact crosstalk in myographic signals.

Detection of the electromechanical delay and its components during voluntary isometric contraction of the quadriceps femoris muscle

Electromechanical delay (EMD) was described as a time elapsed between first trigger and force output. Various results have been reported based on the measurement method with observed inconsistent results when the trigger is elicited by voluntary contraction. However, mechanomyographic (MMG) sensor placed far away on the skin from the contracting muscle was used to detect muscle fiber motion and excitation-contraction (EC) coupling which may give unreliable results. On this basis, the purpose of this study was to detect EMD during active muscle contraction whilst introducing an ultrafast ultrasound (US) method to detect muscle fiber motion from a certain depth of the muscle. Time delays between onsets of EMG-MMG, EMG-US, MMG-FORCE, US-FORCE, and EMG-FORCE were calculated as 20.5 ± 4.73, 28.63 ± 6.31, 19.21 ± 6.79, 30.52 ± 8.85, and 49.73 ± 6.99 ms, respectively. Intrarater correlation coefficient (ICC) was higher than MMG when ultrafast US was used for detecton of the Δt EMG-US and Δt US-FORCE, ICC values of 0.75 and 0.70, respectively. Synchronization of the ultrafast ultrasound with EMG and FORCE sensors can reveal reliable and clinically useful results related to the EMD and its components when muscle is voluntarily contracted. With ultrafast US, we detect onset from the certain depth of the muscle excluding the tissues above the muscle acting as a low-pass filter which can lead to inaccurate time detection about the onset of the contracting muscle fibers. With this non-invasive technique, understanding of the muscle dynamics can be facilitated.

Analysis of crosstalk in the mechanomyographic signals generated by forearm muscles during different wrist postures

INTRODUCTION

In this study, we analyzed the crosstalk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles of the forearm during wrist flexion (WF) and extension (WE) and radial (RD) and ulnar (UD) deviations.

METHODS

Twenty right-handed men (mean ± SD age=26.7 ± 3.83 years) performed the wrist postures. During each wrist posture, MMG signals were detected using 3 accelerometers. Peak cross-correlations were used to quantify crosstalk.

RESULTS

The level of crosstalk ranged from 1.69 to 64.05%. The wrist postures except the RD did not influence the crosstalk significantly between muscle pairs. However, muscles of the forearm compartments influenced the level of crosstalk for each wrist posture significantly.

CONCLUSIONS

The results may be used to improve our understanding of the mechanics of the forearm muscles during wrist postures.

Longitudinal, lateral and transverse axes of forearm muscles influence the crosstalk in the mechanomyographic signals during isometric wrist postures

Problem Statement

In mechanomyography (MMG), crosstalk refers to the contamination of the signal from the muscle of interest by the signal from another muscle or muscle group that is in close proximity.

Purpose

The aim of the present study was two-fold: i) to quantify the level of crosstalk in the mechanomyographic (MMG) signals from the longitudinal (L_o), lateral (L_a) and transverse (T_r) axes of the extensor digitorum (ED), extensor carpi ulnaris (ECU) and flexor carpi ulnaris (FCU) muscles during isometric wrist flexion (WF) and extension (WE), radial (RD) and ulnar (UD) deviations; and ii) to analyze whether the three-directional MMG signals influence the level of crosstalk between the muscle groups during these wrist postures.

Methods

Twenty, healthy right-handed men (mean ± SD: age = 26.7±3.83 y; height = 174.47±6.3 cm; mass = 72.79±14.36 kg) participated in this study. During each wrist posture, the MMG signals propagated through the axes of the muscles were detected using three separate tri-axial accelerometers. The x-axis, y-axis, and z-axis of the sensor were placed in the L_o, L_a, and T_r directions with respect to muscle fibers. The peak cross-correlations were used to quantify the proportion of crosstalk between the different muscle groups.

Results

The average level of crosstalk in the MMG signals generated by the muscle groups ranged from: 34.28–69.69% for the L_o axis, 27.32–52.55% for the L_a axis and 11.38–25.55% for the T_r axis for all participants and their wrist postures. The T_r axes between the muscle groups showed significantly smaller crosstalk values for all wrist postures [F (2, 38) = 14–63, p<0.05, η² = 0.416–0.769].

Significance

The results may be applied in the field of human movement research, especially for the examination of muscle mechanics during various types of the wrist postures.

Cross-talk in mechanomyographic signals from the forearm muscles during sub-maximal to maximal isometric grip force

PURPOSE

This study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles with the sub-maximal to maximal isometric grip force, and with the anthropometric parameters of the forearm, and ii) to quantify the distribution of the cross-talk in the MMG signal to determine if it appears due to the signal component of intramuscular pressure waves produced by the muscle fibers geometrical changes or due to the limb tremor.

METHODS

Twenty, right-handed healthy men (mean ± SD: age  = 26.7±3.83 y; height  = 174.47±6.3 cm; mass  = 72.79±14.36 kg) performed isometric muscle actions in 20% increment from 20% to 100% of the maximum voluntary isometric contraction (MVIC). During each muscle action, MMG signals generated by each muscle were detected using three separate accelerometers. The peak cross-correlations were used to quantify the cross-talk between two muscles.

RESULTS

The magnitude of cross-talk in the MMG signals among the muscle groups ranged from, R2(x, y) = 2.45-62.28%. Linear regression analysis showed that the magnitude of cross-talk increased linearly (r2 = 0.857-0.90) with the levels of grip force for all the muscle groups. The amount of cross-talk showed weak positive and negative correlations (r2 = 0.016-0.216) with the circumference and length of the forearm respectively, between the muscles at 100% MVIC. The cross-talk values significantly differed among the MMG signals due to: limb tremor (MMGTF), slow firing motor unit fibers (MMGSF) and fast firing motor unit fibers (MMGFF) between the muscles at 100% MVIC (p<0.05, η2 = 0.47-0.80).

SIGNIFICANCE

The results of this study may be used to improve our understanding of the mechanics of the forearm muscles during different levels of the grip force.

Bulk movement included in multi-channel mechanomyography: similarity between mechanomyography of resting muscle and that of contracting muscle

Although mechanomyography (MMG) reflects local vibrations from contracting muscle fibers, it also includes bulk movement: deformation in global soft tissue around measuring points. To distinguish between them, we compared the multi-channel MMG of resting muscle, which dominantly reflected the bulk movement caused by arterial pulsations, to that of the contracting muscle. The MMG signals were measured at five points around the upper arms of 10 male subjects during resting and during isometric ramp contraction from 5% to 85% of maximal voluntary contraction (MVC) of the biceps brachii muscle. The characteristics of bulk movement were defined as the amplitude distribution and phase relation among the five MMG signals. The bulk movement characteristics during the rest state were not necessarily the same among the subjects. However, below 30 Hz, each subject's characteristics remained the same from the rest state (0% MVC) to the contracting state (80% MVC), at which the bulk movement mainly originates from muscle contraction activity. Results show that the MMG of the low frequency domain (<30 Hz) includes bulk movement depending on the mechanical deformation characteristics of each subject's body, for a wide range of muscle contraction intensities.