Mechanomyography and tissue oxygen saturation during electrically-evoked wrist extensor fatigue in people with tetraplegia.

BACKGROUND: Repetitive electrically-evoked muscle contractions lead to the early onset of muscle fatigue. This study assessed the relationship between muscle mechanomyography (%RMS-MMG) and tissue oxygen saturation (%TSI) in extensor carpi radialis (ECR) during electrically-evoked fatiguing exercise in individuals with tetraplegia. METHODS: Skin-surface mechanomyography (MMG) and near-infrared spectroscopy (NIRS) sensors were placed on the ECR of seven individuals with tetraplegia. All participants performed repetitive electrically-evoked wrist extension to fatigue while their muscle MMG and NIRS responses were monitored against their power output (PO). FINDINGS: One out of seven participants showed no changes in %TSI throughout the repeated wrist FES-evoked contraction. The other six participants' %TSI was positively correlated with %PO before fatigue onset. At 50%POpeak , %TSI was negatively correlated (0.489) significantly with declining %PO as the ability of the muscle to take up oxygen became limited. The %RMS-MMG behaved analogously during pre and post-fatigue against declining %PO, whereby both displayed positive correlations of 0.443 and 0.214, respectively, (%RMS-MMG decreased) throughout the exercise session. Regression analysis revealed that %TSI was proportional to pre-fatigue and inversely proportional to %RMS-MMG during post-fatigue. CONCLUSION: The significant changes in muscle mechanomyography and tissue oxygenation correlations after 50%POpeak implied that the muscle contraction mechanical-and-physiological behavior association had been altered following FES-evoked fatigue.

Mechanomyography-based assessment during repetitive sit-to-stand and stand-to-sit in two incomplete spinal cord-injured individuals.

Standing up and sitting down movements are important prerequisites to achieve functional independence in everyday life of spinal cord injury (SCI) patients. Thus, mechanomyography (MMG) was proposed as a safe monitoring tool to evaluate muscle function performance of these activities. Two incomplete SCI participants performed repetitive sit-to-stand (SitTS) and stand-to-sit (StandTS) until fatigued challenge. Three sets of these activities were completed with MMG sensors attached over the quadriceps muscles of both legs. A 5-min rest was allocated between each set, totalling 399 trials of SitTS and StandTS activities. There was a significant difference between MMG's mean root mean square maximum (RMSmax) in SitTS and StandTS activities (p = 0.014). The mean values of RMSmax and root mean square average (RMSave) on the right leg were detected to be significantly higher compared to those of the left leg during these activities (p < 0.05). In the frequency domain, MMG's mean power frequency (MPF) proved to be significantly higher at the beginning compared to the end of the three sets of both activities (p = 0.000). These muscle performances during both activities can be verified based on the analysis of MMG behaviour in time and frequency domains. Furthermore, this study indicated that MMG can be used as a monitoring tool to identify muscle fatigue throughout a prolonged event.

Electrically evoked wrist extensor muscle fatigue throughout repetitive motion as measured by mechanomyography and near-infrared spectroscopy.

Repetitive electrically-evoked muscle contraction leads to accelerated muscle fatigue. This study assessed electrically-evoked fatiguing muscle with changes to mechanomyography root mean square percentage (%RMS-MMG) and tissue saturation index (%TSI) in extensor carpi radialis. Forty healthy volunteers (n=40) performed repetitive electrical-evoked wrist extension to fatigue and results were analyzed pre- and post-fatigue, i.e. 50% power output (%PO) drop. Responses of %PO, %TSI and %RMS-MMG were correlated while the relationships between %RMS-MMG and %TSI were investigated using linear regression. The %TSI for both groups were negatively correlated with declining %PO as the ability of the muscle to take up oxygen became limited due to fatigued muscle. The %RMS-MMG behaved in two different patterns post-fatigue against declining %PO whereby; (i) group A showed positive correlation (%RMS-MMG decreased) throughout the session and (ii) group B demonstrated negative correlation (%RMS-MMG increased) with declining %PO until the end of the session. Regression analysis showed %TSI was inversely proportional to %RMS-MMG during post-fatigue in group A. Small gradients in both groups suggested that %TSI was not sensitive to the changes in %RMS-MMG and they were mutually exclusive. Most correlation and regression changed significantly post-fatigue indicating that after fatigue, the condition of muscle had changed mechanically and physiologically.

Neural Network-Based Muscle Torque Estimation Using Mechanomyography During Electrically-Evoked Knee Extension and Standing in Spinal Cord Injury.

This study sought to design and deploy a torque monitoring system using an artificial neural network (ANN) with mechanomyography (MMG) for situations where muscle torque cannot be independently quantified. The MMG signals from the quadriceps were used to derive knee torque during prolonged functional electrical stimulation (FES)-assisted isometric knee extensions and during standing in spinal cord injured (SCI) individuals. Three individuals with motor-complete SCI performed FES-evoked isometric quadriceps contractions on a Biodex dynamometer at 30° knee angle and at a fixed stimulation current, until the torque had declined to a minimum required for ANN model development. Two ANN models were developed based on different inputs; Root mean square (RMS) MMG and RMS-Zero crossing (ZC) which were derived from MMG. The performance of the ANN was evaluated by comparing model predicted torque against the actual torque derived from the dynamometer. MMG data from 5 other individuals with SCI who performed FES-evoked standing to fatigue-failure were used to validate the RMS and RMS-ZC ANN models. RMS and RMS-ZC of the MMG obtained from the FES standing experiments were then provided as inputs to the developed ANN models to calculate the predicted torque during the FES-evoked standing. The average correlation between the knee extension-predicted torque and the actual torque outputs were 0.87 ± 0.11 for RMS and 0.84 ± 0.13 for RMS-ZC. The average accuracy was 79 ± 14% for RMS and 86 ± 11% for RMS-ZC. The two models revealed significant trends in torque decrease, both suggesting a critical point around 50% torque drop where there were significant changes observed in RMS and RMS-ZC patterns. Based on these findings, both RMS and RMS-ZC ANN models performed similarly well in predicting FES-evoked knee extension torques in this population. However, interference was observed in the RMS-ZC values at a time around knee buckling. The developed ANN models could be used to estimate muscle torque in real-time, thereby providing safer automated FES control of standing in persons with motor-complete SCI.