MMG Signals Research Articles

Mechano-responses of quadriceps muscles evoked by transcranial magnetic stimulation.

Mechanomyography (MMG) may be used to quantify very small motor responses resulting from muscle activation, voluntary or involuntary. The purpose of this study was to investigate the MMG mean peak amplitude (MPA) and area under the curve (AUC) and the corresponding mechanical responses following delivery of transcranial magnetic stimulation (TMS) to the knee extensors. Fourteen adults (23±1 years) received single TMS pulses at intensities from 30-80 % maximum stimulator output to elicit muscle responses in the relaxed knee extensors while seated. An accelerometer-based sensor was placed on the rectus femoris (RF) and vastus lateralis (VL) muscle bellies to measure the MMG signal. Pearson correlation revealed a positive linear relationship between MMG MPA and TMS intensity for RF (r=0.569; p<0.001) and VL (r=0.618; p<0.001). TMS intensity of≥60 % maximum stimulator output produced significantly higher MPA than at 30 % TMS intensity and evoked measurable movement at the knee joint. MMG MPA was positively correlated to AUC (r=0.957 for RF and r=0.603 for VL; both p<0.001) and knee extension angle (r=0.596 for RF and r=0.675 for VL; both p<0.001). In conclusion, MMG captured knee extensor mechanical responses at all TMS intensities with the response increasing with increasing TMS intensity. These findings suggest that MMG can be an additional tool for assessing muscle activation.

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.

Estimating Rotational Acceleration in Shoulder and Elbow Joints Using a Transformer Algorithm and a Fusion of Biosignals.

In the present study, we used a transformer model and a fusion of biosignals to estimate rotational acceleration in elbow and shoulder joints. To achieve our study objectives, we proposed a mechanomyography (MMG) signal isolation technique based on a variational mode decomposition (VMD) algorithm. Our results show that the VMD algorithm delivered excellent performance in MMG signal extraction compared to the commonly used technique of empirical mode decomposition (EMD). In addition, we found that transformer models delivered estimates of joint acceleration that were more precise than those produced by mainstream time series forecasting models. The average R2 values of transformer are 0.967, 0.968, and 0.935, respectively. Finally, we found that using a fusion of signals resulted in more precise estimation performance compared to using MMG signals alone. The differences between the average R2 values are 0.041, 0.053, and 0.043, respectively. Taken together, the VMD isolation method, the transformer algorithm and the signal fusion technique described in this paper can be seen as supplying a robust framework for estimating rotational acceleration in upper-limb joints. Further study is warranted to examine the effectiveness of this framework in other musculoskeletal contexts.

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

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

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.

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

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

145 Incorporating Intraoperative Mechanomyography to Peripheral Nerve Decompression Surgery

INTRODUCTION: Considerable progress has been made in the development of novel intraoperative nerve monitoring technologies. Recent studies demonstrated the high sensitivity of mechanomyography (MMG) in detecting nerve function as compared to traditional electromyographic recording. MMG reflects the mechanical vibrations of single motor units detected via accelerometer sensors and is therefore indicative of underlying axonal integrity. METHODS: A total of 20 consecutive patients undergoing decompression procedures for ulnar nerve (8 patients) or common peroneal nerve (12 patients) neuropathies were included. Four patients did not have MMG signals elicited: 1 ulnar and 3 common peroneal nerve patients. Intraoperatively, the nerves were stimulated via MMG ball tip electrode probe starting at 0.1 mA threshold and increasing by 0.1 mA increments until target muscle activity was noted. The lowest threshold current required to elicit a muscle response was recorded prior to decompression and following proximal and distal decompression. RESULTS: Of the patients, 80% (16/20) had MMG signals detected and recorded. The mean pre-decompression stimulus threshold was 1.59 ± 0.77 mA. Following surgical decompression, a statistically significant improvement in mean MMG stimulus threshold was noted (0.47 mA ± 0.13, p &lt; 0.0001). The average stimulus threshold change for the common peroneal nerve patients was -0.80 mA (p = 0.001), and -1.48 mA (p = 0.002) for ulnar nerve patients. Post-operatively, all patients endorsed symptomatic improvement with no complications. CONCLUSIONS: MMG is an established technique that may provide objective intraoperative guidance in determining the extent of surgical nerve decompression. Lower stimulus thresholds likely represent increased sparing of axonal tissue, which may correlate with improved functional recovery. Future work should focus on validating normative values of MMG stimulus thresholds in various nerves and establishing clinical associations with functional outcomes.

A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation.

A wireless multi-layered sensor that allows electromyography (EMG), mechanomyography (MMG) and near-infrared spectroscopy (NIRS) measurements to be carried out simultaneously is presented. The multi-layered sensor comprises a thin silver electrode, transparent piezo-film and photosensor. EMG and MMG measurements are performed using the electrode and piezo-film, respectively. NIRS measurements are performed using the photosensor. Muscular activity is then analyzed in detail using the three types of data obtained. In experiments, the EMG, MMG and NIRS signals were measured for isometric ramp contraction at the forearm and cycling exercise of the lateral vastus muscle with stepped increments of the load using the layered sensor. The results showed that it was possible to perform simultaneous EMG, MMG and NIRS measurements at a local position using the proposed sensor. It is suggested that the proposed sensor has the potential to evaluate muscular activity during exercise, although the detection of the anaerobic threshold has not been clearly addressed.

Incorporating Intraoperative Mechanomyography to Peripheral Nerve Decompression Surgery.

Mechanomyography (MMG) is a novel intraoperative tool to detect and quantify nerve activity with high sensitivity as compared with traditional electromyographic recordings. MMG reflects the mechanical vibrations of single motor units detected through accelerometer sensors after direct motor neuron stimulation. To determine the feasibility of applying intraoperative MMG during peripheral nerve surgery. A total of 20 consecutive patients undergoing surgical decompression of the ulnar nerve at the cubital tunnel or common peroneal nerve at the fibular head were included in this study. Intraoperatively, the common peroneal and ulnar nerves were directly stimulated through the MMG electrode probe starting at 0.1 mA threshold and increasing by 0.1 mA increments until target muscle activity was noted. The lowest threshold current required to elicit a muscle response was recorded before decompression and after proximal and distal nerve decompression. Of the patients, 80% (16/20) had MMG signals detected and recorded. Four patients were unable to have MMG signal detected despite direct nerve visualization and complete neurolysis. The mean predecompression stimulus threshold was 1.59 ± 0.19 mA. After surgical decompression, improvement in the mean MMG stimulus threshold was noted (0.47 ± 0.03 mA, P = .0002). Postoperatively, all patients endorsed symptomatic improvement with no complications. MMG may provide objective guidance for the intraoperative determination of the extent of nerve decompression. Lower stimulus thresholds may represent increased sparing of axonal tissue. Future work should focus on validating normative values of MMG stimulus thresholds in various nerves and establishing clinical associations with functional outcomes.

MMG Signal Analysis for Muscle Performance Assessment

Skeletal muscles' activity can be measured via electromyography (EMG) and mechanomyography (MMG): neural drive and muscle state information influence both signals. Hence, both EMG and MMG can be utilized to investigate neuromuscular physiology in different aspects. In this paper, using the mechanomyogram (MMG) signal, a Digital Signal Processor (DSP) model was created in Simulink to study and evaluate muscle performance. DSPs allow the conversion of real-world phenomena into digital data for computerized analysis and manipulation. The MMG signal, which represents a low frequency vibrational signal recorded when a muscle is contracted, is used as an input by the runtime simulator to evaluate muscle performance. As a result, the DSP simulator will not only display graphs and values that represent muscle strength, but it will also allow for greater flexibility in treatment modalities and provide significant support for the anticipated therapeutic and functional treatment outcome of Neuromuscular Electrical Stimulation (NMES) rehabilitation intervention of disabled people.

Optically pumped magnetometers detect altered maximal muscle activity in neuromuscular disease.

Optically pumped magnetometers (OPM) are quantum sensors that enable the contactless, non-invasive measurement of biomagnetic muscle signals, i.e., magnetomyography (MMG). Due to the contactless recording, OPM-MMG might be preferable to standard electromyography (EMG) for patients with neuromuscular diseases, particularly when repetitive recordings for diagnostic and therapeutic monitoring are mandatory. OPM-MMG studies have focused on recording physiological muscle activity in healthy individuals, whereas research on neuromuscular patients with pathological altered muscle activity is non-existent. Here, we report a proof-of-principle study on the application of OPM-MMG in patients with neuromuscular diseases. Specifically, we compare the muscular activity during maximal isometric contraction of the left rectus femoris muscle in three neuromuscular patients with severe (Transthyretin Amyloidosis in combination with Pompe's disease), mild (Charcot-Marie-Tooth disease, type 2), and without neurogenic, but myogenic, damage (Myotonia Congenita). Seven healthy young participants served as the control group. As expected, and confirmed by using simultaneous surface electromyography (sEMG), a time-series analysis revealed a dispersed interference pattern during maximal contraction with high amplitudes. Furthermore, both patients with neurogenic damage (ATTR and CMT2) showed a reduced variability of the MMG signal, quantified as the signal standard deviation of the main component of the frequency spectrum, highlighting the reduced possibility of motor unit recruitment due to the loss of motor neurons. Our results show that recording pathologically altered voluntary muscle activity with OPM-MMG is possible, paving the way for the potential use of OPM-MMG in larger studies to explore the potential benefits in clinical neurophysiology.

Quantifying Arms and Legs Contributions during Repetitive Electrically-Assisted Sit-To-Stand Exercise in Paraplegics: A Pilot Study.

Execution of Sit-to-Stand (SitTS) in incomplete spinal cord injury (SCI) patients involves motor function in both upper and lower extremities. The use of arm support, in particular, is a significant assistive factor while executing SitTS movement in SCI population. In addition, the application of functional electrical stimulation (FES) onto quadriceps and gluteus maximus muscles is one of the prescribed management for incomplete SCI to improve muscle action for simple lower limb movements. However, the relative contribution of upper and lower extremities during SitTS has not been thoroughly investigated. Two motor incomplete SCI paraplegics performed repetitive SitTS to fatigue exercise challenge. Their performance was investigated as a mixed-method case-control study comparing SitTS with and without the assistance of FES. Three sets of SitTS tests were completed with 5-min resting period allocated in between sets, with mechanomyography (MMG) sensors attached over the rectus femoris muscles bilaterally. The exercise was separated into 2 sessions; Day 1 for voluntary SitTS and Day 2 for FES-assisted SitTS. Questionnaires were conducted after every session to gather the participants' input about their repetitive SitTS experience. The analysis confirmed that a SitTS cycle could be divided into three phases; Phase 1 (Preparation to stand), Phase 2 (Seat-off), and Phase 3 (Initiation of hip extension), which contributed to 23% ± 7%, 16% ± 4% and 61% ± 6% of the SitTS cycle, respectively. The contribution of arms and legs during SitTS movement varied in different participants based on their legs' Medical Research Council (MRC) muscle grade. In particular, the applied arm forces start to increase clearly when the leg forces start to decline during standing. This finding is supported by the significantly reduced MMG signal indicating leg muscle fatigue and their reported feeling of tiredness.

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

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. 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. 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). 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.

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 MMGRMS (r = 0.441), an indication of muscle activation.

A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

Mechanomyography (MMG) signals have extensive applications in muscle function assessment and human intention recognition. However, during signal acquisition, MMG signals are easily contaminated by noise and artifacts, which seriously affects the recognition of their characteristics. To address these issues, a novel noise suppression and artifact removal method based on recursive least square (RLS), improved Gray Wolf Optimizer-optimized variable mode decomposition (IGWO-VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. In this paper, the RLS algorithm is first applied to adaptively filter out the power line interference (PLI). Then, IGWO is designed to select the appropriate VMD parameters and use the VMD to decompose the noisy signal into band-limited intrinsic mode functions (BLIMFs). In addition, the BLIMFs are classified into the low-frequency part and high-frequency part according to the given correlation coefficient (CC) threshold value. The effective components of the low-frequency part are identified by the center frequency. Meanwhile, the high-frequency part is decomposed by CEEMDAN, and its effective components are obtained according to the proposed sample entropy threshold range. Finally, the effective components of the low and high-frequency parts are reconstructed to obtain the denoised signal to realize the extraction of useful signals. Simulation experiment results demonstrate that the proposed method outperforms the classical methods and the designed IGWO-VMD method in terms of denoising performance. The effectiveness of the proposed method is verified through the measured MMG signal experiments. The proposed method not only effectively suppresses noise and artifacts but also overcomes the limitations of VMD and CCEMDAN.

Coactivation During Fatiguing, Maximal, Reciprocal, Isokinetic Forearm Flexion And Extension Muscle Actions Using Mechanomyography

Purpose: Mechanomyography (MMG) has rarely been used to examine coactivation responses during fatiguing tasks. Under some conditions, MMG amplitude (AMP) provides insights into motor unit (MU) recruitment, while MMG mean power frequency (MPF) provides a qualitative description of the global firing rate of the unfused activated motor units. Thus, the purpose of the present study was to use MMG to examine coactivation of the agonist and antagonist muscles during fatiguing reciprocal forearm extension (EXT) and flexion (FLEX) muscle actions. Methods: Ten men (mean ± SD: age = 21.6 ± 1.3 yrs; body mass = 80.9 ± 6.5 kg; height = 179.1 ± 5.9 cm) completed 50 consecutive, maximal, reciprocal, isokinetic forearm EXT and FLEX muscle actions at 60 and 180°·s-1. The AMP and MPF contents of the MMG signals from the biceps (BB) and triceps (TB) were recorded simultaneously throughout the fatiguing task. The MMG and torque values were averaged across 5 consecutive repetitions and normalized to the value from repetition five. A 2 (Speed: 60 and 180°·s-1) x 2 (Muscle: TB and BB) x 2 (Movement: EXT and FLEX) x 10 (Repetitions: 5 - 50) RMANOVA was used to determine mean differences for AMP and MPF. A 2 (Speed) x 2 (Movement) x 10 (Repetitions) RMANOVA was used to determine mean differences for torque. Results: The statistical analyses indicated that for EXT, MMG AMP was greater at 180°·s-1 than 60°·s-1 (p = 0.04) for both the TB and BB. For FLEX, MMG AMP for the TB was greater at 180°·s-1 than 60°·s-1 (p < 0.01), however, there was no significant difference between speeds for the BB. For MMG MPF during EXT, the TB was significantly greater than the BB (p < 0.01). For FLEX, there was no significant difference between muscles. There were significant (p < 0.05) reductions in torque throughout the fatiguing task for both EXT and FLEX. Conclusion: The results indicated that during forearm EXT and FLEX, the faster velocity muscle actions exhibited greater lateral oscillations than the slower velocities, likely due to differences in the contributions of fast and slow fibers to torque production. In addition, the TB exhibited a greater global MU firing rate, as suggested by the MMG MPF responses, than the BB during forearm EXT. Thus, the fatigued-induced decrease in torque was not attributable to changes in coactivation.

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.

Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition

Mechanomyography (MMG) is a kind of biomedical signal with great research value. This paper designed a novel wireless MMG signal acquisition system composed of modular nodes and core board. The modular nodes were worn on different muscle of the lower limbs, collecting movement data of the corresponding part, and transmitted it to the core board wirelessly. The core board was connected to the computer through the USB to achieve the wireless collection and real-time display of MMG signals. In order to improve the real-time performance, this paper adopted four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three improved algorithms (IPSO, IWOA, ISSA) for feature selection to reduce the redundancy of information. In this study, the different effects of different feature selection methods on the recognition of eight types of lower limb movements based on MMG signals were discussed by comparing the classification accuracy, number of iterations and calculation time. The results show that swarm intelligence algorithms have merits in this type of feature selection problem, and GA has the most obvious effect in improving classification accuracy, but it requires more iterations and calculation time, while the classification accuracy of IPSO is close to that of GA, and it has advantages in time consumption.

Determining voluntary activation in synergistic muscles: a novel mechanomyographic approach

PurposeDrawing 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.MethodsTwenty-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 (VAMMG), defined as the superimposed/potentiated MMG peak-to-peak ratio, was determined from the MMG signal for each head.ResultsVAMMG presented a very high intraclass correlation coefficient (0.981–0.998) and sensitivity (MDC95%: 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]. VAMMG was decreased in both the exercising [~ −9(6)%, ES −1.77] and contralateral limb [~ −3(2)%, ES −0.57], with a greater decrease in VAMMG noted only in the vastus medialis of the exercising limb. Moderate-to-very high correlations were found between VAMMG and VA (R-range: 0.503–0.886) before and after exercise.ConclusionVAMMG may be implemented to assess VA and provide further information when multiple synergistic muscle heads are involved in fatiguing exercises.

How Paretic and Non-Paretic Ankle Muscles Contract during Walking in Stroke Survivors: New Insight Using Novel Wearable Ultrasound Imaging and Sensing Technology.

Abnormal muscle tone and muscle weakness are related to gait asymmetry in stroke survivors. However, the internal muscle morphological changes that occur during walking remain unclear. To address this issue, this study investigated the muscle activity of the tibialis anterior (TA) and medial gastrocnemius (MG) of both the paretic and non-paretic sides during walking in nine stroke survivors, by simultaneously capturing electromyography (EMG), mechanomyography (MMG), and ultrasound images, and using a validated novel wearable ultrasound imaging and sensing system. Statistical analysis was performed to examine the test–retest reliability of the collected data, and both the main and interaction effects of each “side” (paretic vs. non-paretic) and “gait” factors, in stroke survivors. This study observed significantly good test–retest reliability in the collected data (0.794 ≤ ICC ≤ 0.985), and significant differences existed in both the side and gait factors of the average TA muscle thickness from ultrasound images, and in the gait factors of TA and MG muscle’s MMG and EMG signals (p < 0.05). The muscle morphological characteristics also appeared to be different between the paretic and non-paretic sides on ultrasound images. This study uncovered significantly different internal muscle contraction patterns between paretic and non-paretic sides during walking for TA (7.2% ± 1.6%) and MG (5.3% ± 4.9%) muscles in stroke survivors.