A non-invasive, quantitative method for detection of muscle fatigue and neural mechanism of compensation during muscle fatigue

Muscle fatigue is less studied in dynamic tasks than in isometric contractions. We analysed muscle activity (sEMG) parameters (Median Frequency and Root Mean Square) during 4 sets of dynamic (15 curls/30s) and static (30s hold at 90° elbow) tasks with a 4kg dumbbell, with rest decreasing from 90 to 30s. sEMG data from 8 upper-limb muscles were collected from healthy participants (19–39 years, 5 females). Perceived exertion (Borg RPE) recorded in all and pre/post task MVC were in 6 participants. Borg RPE significantly increased from set 1 to set 4 (8±1.7; P<0.0001), with MVC decreasing by 24.1% post-fatigue, indicating our protocol induced fatigue. Significant MDF decreases and RMS increases (P<0.0001) were observed. MDF/RMS gradients varied across and within muscles, revealing three fatigability levels and varied fatigue onset. Curl-cycle analysis highlighted phase-specific activation changes, with synergistic muscles showing divergent patterns, reflecting heterogeneous neural control. We developed an indexing method integrating MDF (frequency shifts reflecting motor-unit recruitment/de-recruitment) and RMS (amplitude changes indicating recruitment). This categorizes fatigue states into four quadrants, capturing fatigue progression across tasks and muscles. It offers a simplified clinical tool, providing a holistic view of muscle activity, simplifying interpretation, minimizing misinterpretation risks, thus enhancing visualization of fatigability. This method elucidates compensatory neural mechanisms more effectively than traditional sEMG analysis. Fatigability, defined by MDF/RMS gradients, clarifies fibre-specific effects and neural dynamics. Future work will explore the source of inter-muscular variability extending it to pathological populations. Thus, enhancing understanding of fatigue mechanisms while offering tools for clinical and sports applications.