ECSS Paris 2023: CP-AP25
INTRODUCTION: Neuromuscular fatigability can be defined as an acute reduction in maximal voluntary contraction (MVC) force, englobing neural and intramuscular factors. When assessed in a laboratory environment, the origin and extent of fatigability are known to be sex-dependent. Recently, a portable device (Myocene(R)) has been developed for on-field evaluation, which allows quantifying potential sex difference in fatigability outside the laboratory. This study aimed at comparing a field device to a laboratory standard (femoral nerve supramaximal stimulation) in characterizing knee extensor fatigability after repeated contractions in men vs. women. METHODS: Twenty young, healthy volunteers (10F) performed an intermittent isometric fatigue protocol (5s/5s, 50% maximal voluntary contraction (MVC) force) until failure or for a maximum of 120 repetitions, followed by a 1-min sustained MVC with the knee extensors. Knee extensor neuromuscular function including MVC force, maximal voluntary activation level (VAL) derived from the twitch interpolation technique, force response to 100 and 10 Hz paired stimuli (PS100 and PS10) on relaxed muscles and estimates of prolonged low-frequency force depression (PLFFD) (PS10/PS100 ratio vs. the Powerdex index(R), a submaximal-intensity low-to-high frequency evoked force ratio) were quantified before, immediately after and 10 min after exercise. Measurements were compared between the portable device equipped with a built-in stimulator and the laboratory isometric chair equipped with a strain gauge and coupled with supramaximal femoral nerve stimulation. Linear mixed models (sex x time x Device) were used to analyze MVC force, VAL, PS100 and PLFFD. RESULTS: The protocol induced a significant MVC drop in men (-24.7 +- 13.2%, p < 0.001), while the reduction was not significant in women (-13.6 +- 14.3%, p = 0.064). While voluntary activation decreased similarly between sexes, PS100 dropped only in men (-25.9 +- 11.2%, p<0.001) but not in women (-19.4 +- 8.1%, p = 0.725). When expressed as relative changes from baseline, PLFFD dynamics were similar between devices (Time x Device interaction, p = 0.90), although the PS10/PS100 estimated a significantly greater magnitude of fatiguability (-24.6 +- 14.6%) compared to the Powerdex(R) (-18.7 +- 11.2 %, Device effect, p = 0.004), with no sex difference. CONCLUSION: Our findings show that the lower fatigability in women vs. men can be similarly tracked by the portable device and the laboratory standard. Thus, despite absolute offsets in raw values, the field device provides a valid estimation of fatigability and is sensitive enough to detect sex difference in fatigability.
Read CV Ilan CassoliECSS Paris 2023: CP-AP25
INTRODUCTION: Muscle fatigue (MF) is a pivotal factor in sports performance, yet its real-time estimation remains challenging as conventional methods rely on subjective scales or post-exercise analysis [1]. Surface electromyography (sEMG) provides objective insights into motor unit recruitment and conduction velocity [2], which correlate with MF, leading to the exploration of multimodal AI-driven algorithms that integrate mechanical and physiological data for real-time MF detection. The development of such algorithms requires large amounts of data, and establishing reliable protocols to induce and objectively quantify fatigue is crucial. To addresses this gap, the validation of an experimental protocol designed to induce MF in a consistent manner is presented, serving as a preliminary stage to ensure the data reliability of the future AI-driven system. METHODS: Six males (20.33 ± 1.37 years) participated in this proof-of-concept study. Using a variable-cam leg-extension machine, subjects performed two sets of repetitions until task failure. sEMG was recorded from the vastus lateralis and rectus femoris (PLUX Biosignals). Rating of Perceived Exertion (RPE) was collected every 5 repetitions (Borg-CR10). Signal processing included a 2nd order Butterworth bandpass filter (20-250Hz) and extraction of time-domain (Root Mean Square - RMS) and frequency-domain (Median Frequency - MDF, Mean Frequency - MNF, and Zero Crossing Rate - ZCR) features. RESULTS: A consistent upward trend in RMS was observed (p < 0.05) across all subjects, indicating increased motor unit recruitment, which is consistent with the onset of MF. Frequency-domain analysis showed a significant shift toward lower frequencies, with MDF and MNF decreasing by approximately 30% from the first to the last repetition. ZCR also decreased, confirming the slowing of muscle fiber conduction velocity. The RPE scores correlate with these findings, showing a consistent increase that reached the maximum level (10) at task failure. CONCLUSION: The results showed that this protocol effectively induces localized MF, with high convergence between neuromuscular indicators and subjective perception. The consistency of the signals shows that data originated from this protocol is suitable for training AI models for automated MF detection, potentially enabling the future transformation of conventional gym equipment into "smart" platforms capable of providing real-time estimation and biofeedback for performance optimization and injury prevention. References: [1] J. Yu, et al., “Exploration and Application of a Muscle Fatigue Assessment Model Based on NMF for Multi-Muscle Synergistic Movements,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.32, 2024 [2] S. Wang, et al., “A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework,” IEEE Transactions on Neural Networks and Learning Systems, vol.34, 2023
Read CV Letícia Costa de SousaECSS Paris 2023: CP-AP25