Changes of agonist and synergist muscles activity during a sustained submaximal brake-pulling gesture

Multilevel Assessment of Exercise Fatigue Utilizing Multiple Attention and Convolution Network (MACNet) based on Surface Electromyography

BACKGROUND

Assessment of exercise fatigue is crucial for enhancing work capacity and minimizing the risk of injury. Surface electromyography (sEMG) has been used to quantitatively assess exercise fatigue as a new technology in recent years. However, the currently available research primarily distinguishes between fatigue and non-fatigue states, offering limited and less robust findings in multilevel evaluations.

METHODS

This study proposes a multiple attention and convolution network (MACNet) for a three-level assessment of muscle fatigue based on sEMG. Under the designed 50% maximum voluntary contraction experimental paradigm, sEMG signals and rate of perceived exertion scale are collected from 48 subjects. MACNet is developed to assess sEMG fatigue, incorporating improved temporal attention based on sliding window, multiscale convolution, and channel-spatial attention. Finally, GradCAM visualization is used to verify the developed model's interpretation, exploring the effects of sEMG channels and time-domain characteristics on exercise fatigue.

RESULTS

The average classification F1-Score and accuracy of MACNet are 83.95% and 84.11% for subject-wise and 82.83% and 82.43% for cross-subject, respectively. The GradCAM visualization highlights the greater contribution of the flexor digitorum superficialis and flexor digitorum profundus in evaluating high fatigue, along with the varied impact of time-domain features on exercise fatigue assessment.

CONCLUSION

MACNet achieves the highest average classification accuracy and F1-Score, significantly higher than other state-of-the-art methods like SVM, RF, MFFNet, TSCNN, LMDANet, Conformer and MSFEnet, enhancing the extraction of exercise fatigue insights from sEMG channels and time-domain features.

The Disconnect Between Soccer Players' Perceived and Actual Electromyographic-Measured Muscle Activation

Understanding muscle activation during exercises is crucial for devising effective training programs. We examined correlations between self-reported and electromyographic (EMG) muscle activity during upper-body exercises performed at loads corresponding to 4-6 repetition maximums (RMs). Thirteen male sub-elite soccer players who had previously engaged in resistance training participated in two testing sessions. In the initial session, the loads corresponding to 4-6 repetitions were determined for six exercises: Lat Pull Down (LPD), Barbell Bent Over Row (BBOR), Dumbbell Row (DR), Barbell Pull Over (BPO), Dumbbell Reverse Fly (DRF), and Dumbbell Concentration Curl (DCC). At post-exercise, participants rated their perceived muscle activation for three targeted muscles in each exercise on a 1-10 point Likert scale (LS). In the subsequent session, we used EMG to measure the activity of eight agonist and synergist muscles during these exercises. We found that one of two synergist muscles consistently demonstrated higher activity levels. Interestingly, we observed no difference in activity between primary and secondary (or synergist) muscles across all exercises. Most importantly, we found no significant correlation between the perceived muscle activation rate and the EMG measured activation level for any exercise. In conclusion, our findings suggest that, despite differential muscle activity during specific exercises, self-reported muscle activation may not accurately correspond to actual muscle activation, as measured via EMG, due to the participants' poor interoceptive awareness of muscles. These data highlight the potential limitations of relying on perceived muscle activation as a sole gauge of training intensity.

Neural Adjustments during Repeated Braking and Throttle Actions on a Motorcycle Setup

The aim of the study was to assess neuromuscular changes during an intermittent fatiguing task designed to replicate fundamental actions and ergonomics of road race motorcycling. Twenty-eight participants repeated a sequence of submaximal brake-pulling and gas throttle actions, interspaced by one maximal brake-pulling, until failure. During the submaximal brake-pulling actions performed at 30% MVC, force fluctuations, surface EMG, maximal M-wave (Mmax) and H-reflex were measured in the flexor digitorum superficialis. At the end of the task, the MVC force and associated EMG activity decreased (P<0.001) by 46% and 26%, respectively. During the task, force fluctuation and EMG activity increased gradually (106% and 61%, respectively) with respect to the pre-fatigue state (P≤0.029). The Mmax first phase did not change (P≥0.524), whereas the H-reflex amplitude, normalized to Mmax, increased (149%; P≤0.039). Noteworthy, the relative increase in H-reflex amplitude was correlated with the increase in EMG activity during the task (r=0.63; P<0.001). During the 10-min recovery, MVC force and EMG activity remained depressed (P≤0.05) whereas H-reflex amplitude and force fluctuation returned to pre-fatigue values. In conclusion, contrarily to other studies, our results bring forward that when mimicking motorcycling brake-pulling and gas throttle actions, supraspinal neural mechanisms primarily limit the duration of the performance.