Introduction:Muscle fatigue, a sensation of tiredness and weakness underpinned by various physiological and psychological processes, is a common occurrence in daily life (Allen et al., 2008; González-Izal et al., 2012). In the contexts of sports and rehabilitation, muscle fatigue is an inevitable phenomenon (Mayo et al., 2019; Sánchez-Medina & González-Badillo, 2011). Due to accompanying impairments in force and power-generating capacity, muscle fatigue leads to a decrease in exercise performance, affecting metrics like peak velocity and power output. Additionally, muscle fatigue is associated with an increased risk of acute injuries and chronic soreness (Roy et al., 1989). Therefore, it is crucial for professionals in this field, including coaches and physical therapists, to understand the fatigue conditions of their clients or athletes. Muscle fatigue can be identified through various physiological measures, including surface electromyography (sEMG) (Dimitrov et al., 2006; González-Izal et al., 2010, 2012).  However, due to limitations such as high costs and complex analysis techniques, the widespread use of sEMG-based muscle fatigue assessments in exercise contexts is not feasible.  Muscle fatigue can manifest through subjective feelings of fatigue and tiredness. The Rating of Perceived Exertion (RPE) scale, a perceptual-based assessment method, employs a combination of numerical, verbal, and pictorial descriptors (Borg, 1998; Lagally et al., 2002).  RPE is reported to mirror physiological changes, including heart rate, during exercise (Bautista et al., 2014; Lagally & Robertson, 2006; Robertson et al., 2004).  RPE can also serve as an estimator of muscle fatigue in a variety of exercise contexts.  However, the correlation between RPE and muscle fatigue has been primarily investigated in isometric exercises, as sEMG signals tend to become unstable during dynamic activities (Keller et al., 2018). Newly developed methods based on mathematical simulations for assessing muscle fatigue through sEMG signals are now accessible (Dimitrov et al., 2006).  Therefore, the relationship between RPE and muscle fatigue, particularly from explosive resistance exercises, could be clarified with the use of advanced sEMG processing techniques. Due to its positive impact on athletic performance, velocity-based training has gained significant popularity in resistance exercise scenarios (Mayo et al., 2019; Sánchez-Medina & González-Badillo, 2011). Given the rising popularity of velocity-based training, velocity loss is now considered a reliable indicator of muscle fatigue in resistance exercises, with a broad array of tools available for assessing velocity-based muscle fatigue. Nonetheless, for many independent coaches, the widespread adoption of velocity-based assessments during resistance exercises remains impractical.Therefore, this study aims to: 1) evaluate the validity of RPE in assessing muscle fatigue using novel sEMG-based techniques, and 2) investigate the effectiveness of RPE as an indicator of velocity loss during explosive resistance exercises.  Our hypotheses are as follows: 1) RPE and velocity loss will correspondingly change with increasing muscle fatigue, and 2) a significant relationship between RPE and muscle fatigue will be observed. Additionally, 3) we hypothesize a significant correlation between velocity loss and RPE, suggesting that RPE can serve as an effective, simplified indicator of velocity loss. Methods:Given the technical demands of explosive resistance exercises, we recruited collegiate athletes with experience in resistance training as participants in this study. Based on statistical power analysis (effect size of 0.4, alpha level of 0.05, and a power of 0.95), a minimum of 14 participants was deemed necessary for this study (Zhao et al., 2022, 2023, 2024). Therefore, we aimed to recruit 15 collegiate athletes for the study. The bench press (BP) and back squat (BS) were chosen as the experimental exercises.  In the experimental sessions, participants underwent three conditions with volumes of 30% (low, L), 60% (medium, M), and 90% (high, H), arranged in a counterbalanced order. The volume for each condition was determined by multiplying the participant's one-repetition maximum (1RM) percentage by the designated number of repetitions. The required repetitions for each condition were calculated based on the participants' performance in the pre-measurement session. For all conditions, we recorded the RPE scores, sEMG signals, and movement velocity. Present progression and expected result:As of March 2024, nine participants have been successfully recruited. Their descriptive statistics are as follows (mean ± standard deviation): age, 20.33 ± 0.94 years; body mass, 70.86 ± 7.08 kg; height, 171.30 ± 4.27 cm; body fat percentage, 16.26 ± 3.49%. The one-repetition maximum (1RM) for the bench press (BP) was 90.56 ± 14.85 kg, and for the back squat (BS), it was 128.33 ± 28.96 kg. Analysis of other measured variables is ongoing.Previous research has shown that RPE and average muscle fatigue levels correspondingly change, demonstrating a significant relationship between RPE and muscle fatigue. This suggests that RPE can effectively serve as an indicator of muscle fatigue during non-explosive resistance exercises (Zhao et al., 2022, 2023, 2024). Building upon these findings, we hypothesize that muscle fatigue will elicit similar increases in perceptual responses during explosive BP and BS exercises. We also anticipate observing a significant correlation between sEMG-based measures of muscle fatigue and RPE, reinforcing the utility of RPE as an indicator of muscle fatigue in explosive resistance exercises. Regarding the relationship between velocity loss and RPE, prior studies have suggested that RPE correlates with velocity changes and neuromuscular fatigue parameters during explosive resistance exercises (Mayo et al., 2019; Sánchez-Medina & González-Badillo, 2011). Based on this evidence, we predict a significant correlation between velocity loss and sEMG-based muscle fatigue measures during explosive BP and BS exercises in the current study. Future plan:Due to the slower-than-anticipated pace of participant recruitment, we aim to conclude this study by May 2024. 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