Comparison of displacement and acceleration transducers for the characterization of mechanics of muscle and subcutaneous tissues by system identification of a mechanomyogram

Biomechanical Process of Skeletal Muscle under Training Condition Based on 3D Visualization Technology

With the development and popularization of 3D technology, human behavior recognition has gradually developed from plane feature recognition to elevation feature recognition. In the process of collecting motion characteristics, the research on skeletal muscle will lead to a series of data in time series, which is the basis of sports biomechanics research and sports training. Some important semantic information such as centerline and joint center can be obtained by further data processing. The results of the study showed that the three-dimensional coordinate positions of the femur and pelvic attachment points of the muscles surrounding the hip joint from the pelvis were measured and positioned. A 3D model is built to simulate the human skeletal model subjected to speeds of 3 and 7 m/s, and different motion velocities can exhibit different motions. The research in this study shows that using 3D technology and comprehensively utilizing the expertise of biomechanical analysis and graphical modeling to study the mechanical properties of bone joints and soft tissues provide new ways and methods.

Basic characteristics between mechanomyogram and muscle force during twitch and tetanic contractions in rat skeletal muscles

The mechanomyogram (MMG) is a signal measured by various vibration sensors for slight vibrations induced by muscle contraction, and it reflects the muscle force during electrically induced-contraction or until 60%-70% maximum voluntary contraction, so the MMG is considered an alternative and novel measurement tool for muscle strength. We simultaneously measured the MMG and muscle force in the gastrocnemius (GC), vastus intermedius (VI), and soleus (SOL) muscles of rats. The muscle force was measured by attaching a hook to the tendon using a load cell, and the MMG was measured using a charged-coupled device-type displacement sensor at the middle of the target muscle. The MMG-twitch waveform was very similar to that of the muscle force; however, the half relaxation time and relaxation time (10%), which are relaxation parameters, were prolonged compared to those of the muscle force. The MMG amplitude correlated with the muscle force. Since stimulation frequencies that are necessary to evoke tetanic progression have a significant correlation with the twitch parameter, there is a close relationship between twitch and tetanus in the MMG signal. Therefore, we suggest that the MMG, which is electrically induced and detected by a laser displacement sensor, may be an alternative tool for measuring muscle strength.

Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review

This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.

Scaling Relationship of In Vivo Muscle Contraction Strength of Rabbits Exposed to High-Frequency Nanosecond Pulse Bursts

We studied the influence of various parameters of high-frequency nanosecond pulse bursts on the strength of rabbit muscle contractions. Ten unipolar high-frequency pulse bursts with various field intensities E (1 kV/cm, 4 kV/cm, and 8 kV/cm), intraburst frequencies f (10 kHz, 100 kHz, and 1 MHz), and intraburst pulse numbers N (1, 10, and 100) were applied using a pair of plate electrodes to the surface skin of the rabbits' biceps femoris, and the acceleration signal of muscle contraction near the electrode was measured using a 3-axis acceleration sensor. A time- and frequency-domain analysis of the acceleration signals showed that the peak value of the signal increases with the increasing strength of the pulse burst and that the frequency spectra of the signals measured under various pulse bursts have characteristic frequencies (at approximately 2 Hz, 32 Hz, 45 Hz, and 55 Hz). Furthermore, we processed the data through multivariate nonlinear regression analysis and variance analysis and determined that the peak value of the signal scales with the logarithm to the base 10 of EN ^x, where x is a value that scales with the logarithm to the base 10 of intraburst frequency (f). These results indicate that for high-frequency nanosecond pulse treatment of solid tumors in or near muscles, when the field strength is relatively high, the intraburst frequency and the intraburst pulse number require appropriate selection to limit the strength of muscle contraction as much as possible.

Estimation of tibialis anterior muscle stiffness during the swing phase of walking with various footwear

The current study examined stiffness in the tibialis anterior muscle during the swing phase of walking while wearing various footwear. Seven healthy young men participated in this study. Participants were instructed to walk on a treadmill at 3 km/h while wearing sports shoes, slippers, or slippers with belts. The common peroneal nerve was electrically stimulated every two steps at toe-off during walking. Mechanomyograms (MMGs), electromyograms, and ankle angle were measured. Evoked MMG was extracted using a Kalman filter and subtraction of walking acceleration. The transfer function from the electrical stimulation to the evoked MMG was identified using a singular value decomposition method, and the natural frequency of the transfer function was calculated as an index of muscle stiffness. The natural frequency did not show a clear relationship with footwear type. Four participants showed the lowest natural frequency when they wore slippers with belts. The remaining subjects showed the lowest natural frequency when they wore slippers or shoes. These contrasting findings may have been caused by different degrees of adaptation of participants to the footwear.

Introduction to Control Systems Design Using Matlab

Control systems theory is a wide area covering a range of artificial and physical phenomena. Control systems are systems that are designed to operate under strict specifications, to satisfy certain aims, like safety regulations in the industry, optimal production of goods, disturbance rejection in vehicles, smooth movement and placement of objects in warehousing, regulation of drug administration in medical operations, level control in chemical processes and many more. The present work provides an introduction to the fundamental principles of control system's analysis and design through the programming environment of Matlab and Simulink. Analysis of transfer function models is carried out though multiple examples in Matlab and Simulink, analyzing the dynamics of 1st and 2nd order systems, the role of the poles and zeros in the system's dynamic response, the effects of delay and the possibility to approximate higher order systems by lower order ones. In addition, examples are given from the fields of mechanical systems, medically induced anesthesia, neuroprosthetics and water level control, showcasing the use of controllers that satisfy certain design specifications.

System identification of velocity mechanomyogram measured with a capacitor microphone for muscle stiffness estimation

A mechanomyogram (MMG) measured with a displacement sensor (displacement MMG) can provide a better estimation of longitudinal muscle stiffness than that measured with an acceleration sensor (acceleration MMG), but the displacement MMG cannot provide transverse muscle stiffness. We propose a method to estimate both longitudinal and transverse muscle stiffness from a velocity MMG using a system identification technique. The aims of this study are to show the advantages of the proposed method. The velocity MMG was measured using a capacitor microphone and a differential circuit, and the MMG, evoked by electrical stimulation, of the tibialis anterior muscle was measured five times in seven healthy young male volunteers. The evoked MMG system was identified using the singular value decomposition method and was approximated with a fourth-order model, which provides two undamped natural frequencies corresponding to the longitudinal and transverse muscle stiffness. The fluctuation of the undamped natural frequencies estimated from the velocity MMG was significantly smaller than that from the acceleration MMG. There was no significant difference between the fluctuations of the undamped natural frequencies estimated from the velocity MMG and that from the displacement MMG. The proposed method using the velocity MMG is thus more advantageous for muscle stiffness estimation.

Muscle stiffness estimation using a system identification technique applied to evoked mechanomyogram during cycling exercise

The aims of this study were to develop a method to extract the evoked mechanomyogram (MMG) during cycling exercise and to clarify muscle stiffness at various cadences, workloads, and power. Ten young healthy male participants were instructed to pedal a cycle ergometer at cadences of 40 and 60 rpm. The loads were 4.9, 9.8, 14.7, and 19.6 N, respectively. One electrical stimulus per two pedal rotations was applied to the vastus lateralis muscle at a knee angle of 80° in the down phase. MMGs were measured using a capacitor microphone, and the MMGs were divided into stimulated and non-stimulated sequences. Each sequence was synchronously averaged. The synchronously averaged non-stimulated MMG was subtracted from the synchronously averaged stimulated MMG to extract an evoked MMG. The evoked MMG system was identified and the poles of the transfer function were calculated. The poles and mass of the vastus lateralis muscle were used to estimate muscle stiffness. Results showed that muscle stiffness was 186-626 N /m and proportional to the workloads and power. In conclusion, our method can be used to assess muscle stiffness proportional to the workload and power.

Fatigue effects on the electromechanical delay components during the relaxation phase after isometric contraction

AIM

By a combined electromyographic (EMG), mechanomyographic (MMG) and force (F) analysis, the electromechanical delay during muscle relaxation (R-DelayTOT ) was partitioned into electrochemical and mechanical components. The study aimed to evaluate the effects of fatigue on R-DelayTOT components and to assess their intersession and interday reliability Intraclass correlation coefficient (ICC).

METHODS

During tetanic stimulations, EMG, MMG and F were recorded from the human gastrocnemius medialis muscle before and after fatigue. The latency between EMG and MMG ripple cessations (R-Δt EMG-MMGR , electrochemical R-DelayTOT component); between MMG ripple cessation and F decay onset (R-Δt MMGR -F, first R-DelayTOT mechanical component); and between F decay onset and maximum MMG negative peak (R-Δt F-MMGp-p , second R-DelayTOT mechanical component) was calculated.

RESULTS

Before fatigue, R-Δt F-MMGp-p was the major contributor (61.9 ± 1.7 ms, 75%) to R-DelayTOT (82.7 ± 1.0 ms), while R-Δt EMG-MMGR and R-Δt MMGR -F accounted for 16% (13.3 ± 1.2 ms) and 9% (7.5 ± 1.0 ms) respectively. After fatigue, R-DelayTOT , R-Δt EMG-MMGR and R-Δt MMGR -F increased by 11, 41 and 67%, respectively (P < 0.05), whereas R-Δt F-MMGp-p did not change. Consequently, the relative contribution of R-Δt EMG-MMGR , R-Δt MMGR -F and R-Δt F-MMGp-p , to R-DelayTOT changed to 20 ± 2, 12 ± 1 and 68 ± 2% respectively. Measurement reliability was always from high to very high (ICC 0.705-0.959).

CONCLUSION

Fatigue altered the processes between neuromuscular activation cessation and force decay onset, but not the second mechanical component (cross-bridges detachment rate and series elastic components release). This combined approach provided reliable measurement of the different R-DelayTOT components and it may represent a valid tool to get more insights on muscle electromechanical behaviour.

System identification of evoked mechanomyogram from abductor pollicis brevis muscle in isometric contraction

The purpose of this study is to verify the applicability of a sixth-order model to the mechanomyogram (MMG) system of the parallel-fibered muscle, which was identified from the MMG of the pennation muscle. The median nerve was stimulated, and an MMG and torque of the abductor pollicis brevis muscle were measured. The MMGs were detected with either a capacitor microphone or an acceleration sensor. The transfer functions between stimulation and the MMG and between stimulation and torque were identified by the singular value decomposition method. The torque and the MMG, which were detected with a capacitor microphone, DMMG, were approximated with a second- and a third-order model, respectively. The natural frequency of the torque, reflecting longitudinal mechanical characteristics, did not show a significant difference from that of the DMMG. The MMG detected with an acceleration sensor was approximated with a fourth-order model. The natural frequencies of the AMMG reflecting the muscle and subcutaneous tissue in the transverse direction were obtained. Both DMMG and AMMG have to be measured to investigate the model of the MMG system for parallel-fibered muscle. The MMG system of parallel-fibered muscle was also modeled with a sixth-order model.

Reliability of the Electromechanical Delay Components Assessment during the Relaxation Phase

The study aimed to assess by an electromyographic (EMG), mechanomyographic (MMG), and force-combined approach the electrochemical and mechanical components of the overall electromechanical delay during relaxation (R-EMD). Reliability of the measurements was also assessed. To this purpose, supramaximal tetanic stimulations (50 Hz) were delivered to the gastrocnemius medialis muscle of 17 participants. During stimulations, the EMG, MMG, and force signals were detected, and the time lag between EMG cessation and the beginning of force decay (Δt EMG-F, as temporal indicators of the electrochemical events) and from the initial force decrease to the largest negative peak of MMG signal during relaxation (Δt F-MMG, as temporal indicators of the mechanical events) was calculated, together with overall R-EMD duration (from EMG cessation to the largest MMG negative peak during relaxation). Peak force (pF), half relaxation time (HRT), and MMG peak-to-peak during the relaxation phase (R-MMG p-p) were also calculated. Test-retest reliability was assessed by Intraclass Correlation Coefficient (ICC). With a total R-EMD duration of 96.9 ± 1.9 ms, Δt EMG-F contributed for about 24% (23.4 ± 2.7 ms) while Δt F-MMG for about 76% (73.5 ± 3.2 ms). Reliability of the measurements was high for all variables. Our findings show that the main contributor to R-EMD is represented by the mechanical components (series elastic components and muscle fibres behaviour), with a high reliability level for this type of approach.