Extra-torque of human tibialis anterior during electrical stimulation with linearly varying frequency and amplitude trains

Using force or EMG envelope as feedback signal for motor control system

PURPOSE

This work studied muscle neuro-mechanics during symmetrical up-going ramp (UGR) and down-going ramp (DGR).

AIM

to evaluate during the modulation of muscular action the outcome of force feedback (FF) or neural feedback (NF) on the behavior of the trailing signals - i.e. the EMG envelope (eEMG) for FF or force signal for NF.

METHOD

Subjects: 20. Investigated muscles: dorsal interosseous (FDI) and tibialis anterior (TA). Detected signals: force and EMG. Visual feedback: force (FF), eEMG (NF). Effort triangles: ramps duration 7.5 s, vertex at 50 and 100 % of the maximal voluntary action. Eventually, each subject performed FF50%, FF100%, NF50% and NF100% per each muscle. In each condition the areas beneath the force and eEMG signals were computed to calculate the ratios between the DGR and UGR values during the different tasks (force area DGR / force area UGR; eEMG area DGR / eEMG area UGR). Electro-mechanical coupling efficiency (EMCE) was estimated through the eEMG area / force area ratio for both UGR and DGR in each condition.

RESULTS

a) FF. FDI: eEMG area ratio was 0.84 ± 0.15 and 0.73 ± 0.17 for FF50% and FF100%, respectively. TA: eEMG area ratio was 0.88 ± 0.11 and 0.91 ± 0.17 for FF50% and FF100%, respectively. b) NF: FDI: force area ratio was 1.18 ± 0.13 and 1.17 ± 0.13 for NF50% and NF100%, respectively. TA: force area ratio was 1.17 ± 0.21 and 1.07 ± 0.19 for NF50% and NF100%, respectively. c) DGR EMCE was greater than UGR EMCE in all four tasks.

CONCLUSION

The influence of UGR on deployed EMCE in the following force decrement phase underpins the changes of trailing signals area during DGR. This underlines the necessity of a careful evaluation of the features of FF or NF for experimental studies or rehabilitation purposes involving the motor control system.

Influence of age on force and re-lengthening dynamics after tetanic stimulation withdrawal in the tibialis anterior muscle

PURPOSE

During alternate movements across a joint, the changeover from one direction of rotation to the opposite may be influenced by the delay and rate of tension reduction and the compliance to re-lengthening of the previously active muscle group. Given the aging process may affect the above-mentioned factors, this work aimed to compare the dynamics of both the ankle torque decline and muscle re-lengthening, mirrored by mechanomyogram (MMG), in the tibialis anterior because of its important role in gait.

METHODS

During the relaxation phase, after a supramaximal 35 Hz stimulation applied at the superficial motor point, in 20 young (Y) and 20 old (O) subjects, the torque (T) and MMG dynamics characteristics were measured.

RESULTS

The T and MMG analysis provided: (I) the beginning of the decay after cessation of stimulation (T: 22.51 ± 5.92 ms [Y] and 51.35 ± 15.21 ms [O]; MMG: 27.38 ± 6.93 ms [Y] and 61.41 ± 18.42 ms [O]); (II) the maximum rate of reduction (T: - 110.4 ± 45.56 Nm/s [Y] and - 52.72 ± 32.12 Nm/s [O]; MMG: - 24.47 ± 10.95 mm/s [Y] and - 13.76 ± 6.54 mm/s [O]); (III) the muscle compliance, measuring the MMG reduction of every 10% reduction of torque (bin 20-10%: 15.69 ± 7.5[Y] and 10.8 ± 3.3 [O]; bin 10-0%: 22.12 ± 10.3 [Y] and 17.58 ± 5.6 [O]).

CONCLUSION

Muscle relaxation results are different in Y and O and can be monitored by a non-invasive method measuring physiological variables of torque and re-lengthening dynamics at the end of the electromechanical coupling previously induced by the neuromuscular stimulation.

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.

The age related slow and fast contributions to the overall changes in tibialis anterior contractile features disclosed by maximal single twitch scan

This work aimed to verify if maximal electrically evoked single twitch (STmax) scan discloses the relative functional weight of fast and slow small bundles of fibres (SBF) in determining the contractile features of tibialis anterior (TA) with ageing. SBFs were recruited by TA main motor point stimulation through 60 increasing levels of stimulation (LS): 20 stimuli at 2Hz for each LS. The lowest and highest LS provided the least ST and STmax, respectively. The scanned STmax was decomposed into individual SBF STs. They were identified when twitches from adjacent LS were significantly different and then subtracted from each other. Nine young (Y) and eleven old (O) subjects were investigated. Contraction time (CT) and STarea/STpeak (A/PT) were calculated per each SBF ST. 143 and 155 SBF STs were obtained in Y and O, respectively. Y: CT and A/PT range: 45-105ms and 67-183mNs/mN, respectively. Literature data set TA fast fibres at 34% so, from the arrays of CT and A/PT, 65ms and 100mNs/mN were identified as the upper limit for SBF fast ST classification. O: no SBF ST could be classified as fast.

CONCLUSIONS

STmax scan reveals age-related changes in the relative contribution of fast and slow SBFs to the overall muscle mechanics.