In vivo behaviour of human muscle architecture and mechanomyographic response using the interpolated twitch technique

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.

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.

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.

A technical method using musculoskeletal model to analyse dynamic properties of muscles during human movement

An effective way to avoid invading or injuring the subjects is to use the musculoskeletal model when studying the dynamic properties of muscles in vivo. So, we put forward a joint coordinate system-based method, which mainly focuses on the coordinate's transformation of corresponding muscle attachment points, respectively, in the model and the subject in order to reproduce the movement of the subject on the model. As muscle moment arm is usually used to evaluate the dynamic properties of muscles, the moment arms in elbow flexion for each of the major muscles crossing the elbow in 50 healthy subjects (25 males and 25 females), ranging in height from 1.50 to 1.80 m (mean 1.6542 m) are calculated and compared with the measured data obtained from anatomical studies reported in the literature. The trends of the value basically coincide with each other. So, this novel method can be valid.

Investigation of muscle behavior during different functional electrical stimulation profiles using Mechanomyography

Mechanomyography (MMG) is a technique for measuring muscle oscillations and fatigue. Functional electrical stimulation (FES) has been applied to control movements mainly in people with spinal cord injury (SCI). The goal of this study is the application of the MMG signal as a tool to investigate muscle response during FES. Ten healthy individuals (HI) and three SCI were submitted to four FES profiles in the rectus femoris (RF) and vastus lateralis (VL) muscles. Four FES profiles were applied in different days. The FES profile set to 1 kHz pulse frequency, 200 us active pulse duration and burst frequency of 50 Hz presented the lowest MMG root mean square and spectral median frequency values, suggesting less muscle modification. The MMG signal was different between HI and SCI but there was no difference between the RF and VL muscles.