EMG Processing

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This research activity is focused on

1) The analysis of multivariate electromyographic signals to investigate correlates of muscle fatigue

2) Correlation of EMG features with other autonomic and central nervous system covariates

 

 

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Many studies have been conducted on muscular fatigue. Muscular force production is mainly controlled by two phenomena: the recruitment of additional motor units (MUs) and the increase of firing rate of the already active MUs. These phenomena happen in different proportions in different muscles. Specifically, in some muscles the recruitment of new MUs can be stopped at about 50% of the maximal force, while in others it can be observed until the maximal voluntary contraction force. Similarly to the developed force, surface EMG amplitude depends on both the amount of activated MUs and on their firing rates. Again, since surface EMG amplitude increases as response to the same phenomena as well as force, it is possible to assert that muscle force can be estimated via surface EMG analysis. Anyway, it was not possible to always observe a linear relation between force and EMG amplitude. Sometimes it was described as having a parabolic shape. A possible explanation of such a variety of behaviors can be related to differences in the percentage of recruitment and rate coding.

Notwithstanding, it is important to highlight that other factors can influence these phenomena. In fact, electrode location can severely affect the surface EMG amplitude, and therefore the relation between this feature and developed force may be weaker. Moreover, also if an “optimal” placement of EMG electrodes can be reached, the inclination of the muscular fibers with respect to the detection system, the distributions of the conduction velocities of the active MUs, the distance between electrodes, the presence of crosstalk, and the degree of synchronization can severely influence the relation force/EMG. Again, signals with different amplitude trend can be generated by the same control strategy, since MUs within the muscle are placed in different locations. Hence, considering all these factors, the reliability of any specific EMG-force relation can be debatable. This relation can be very subject- and muscle- specific.

Commonly, median frequency is used as a gold standard to detect it in muscles using EMG signals. Other approaches aimed at investigating heart activity, blood volumes or oxygenation rates during fatiguing exercises. For instance, heart rate has been largely observed to increase in presence of muscular fatigue. In fact, some studies suggested that RR variability might depend on the changes in underlying physiological control system “strategy” under different workload conditions.

In 1984, Sheldahi et al. reported a study of cardiac performance during exercises. They observed a change of heart rate (HR) as well as a change in central blood volume, resulting in a modification in left ventricular end-diastolic and end-systolic dimensions, during moderate levels of exercise. In increasing heart rate, as well as oxygen uptake, was observed during prolonged cycling exercises. Schwane et al. observed that the economy of motion in a novel activity might induce an increasingly need of greater motor unit requirement, resulting in an increasing energy and oxygen demand at the tissue level. A higher HR was observed while walking backward than when walking forward in Masamuto at al.. Moreover they observed an increasing HR at increasing walking speeds. An higher HR and V02 maximum values were also observed during backward walking on a treadmill with respect to the forward walking also in. In the study of Watson, it was suggested that the observed RR variability might depend on the changes in underlying physiological control system “strategy” under different workload conditions.

Interestingly, in the study proposed by Mottram et al., both electromyographic (EMG) activity and autonomic nervous system were monitored during fatiguing exercises. Specifically different rates of change in mean arterial pressure, heart rate, perceived exertion (PE), and fluctuations in motor output were observed during the performance of fatiguing contractions, although similar rates of change in average EMG activity were reported. Moreover, in de Souza Castelo Oliveira et al. EMG, HR and PE were compared during different elbow flexion exercises in standing and seated position. The authors reported that positioning could affect the neuromuscular, cardiovascular, and sensorial responses during resistance elbow flexor exercises. In addition, they also observed changes in EMG and PE, while slighter effects in the HR, due to different load levels, and significantly differences in EMG, HR and PE data due to different exercise durations. Respiratory-sinus-arrhythmia-related power ratio in the fluctuation of the R-R interval and a muscular fatigue index were used to classify physical activity during cycling.

Notwithstanding the many study on the relation between heart activity and muscular fatigue and/or exercise, at our knowledge, no investigation has been performed by taking into account the parameters of heart rate variability, and hence providing a deeper characterization of heart rate variability.

 

Leo, A., Handjaras, G., Bianchi, M., Marino, H., Gabiccini, M., Guidi, A., Scilingo, E. P., Pietrini, P., Bicchi, A., Santello, M., Ricciardi, E. (2016). A synergy-based hand control is encoded in human motor cortical areas. eLife, 5, e13420. http://dx.doi.org/10.7554/elife.13420. (journal)

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Computational Physiology & Biomedical Instruments Group, University of Pisa

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