Muscle fatigue's indirect link to higher athletic injury risks is a key focus of this study. It highlights how fatigue-induced shifts in muscle resource allocation and movement patterns can lead to biomechanical imbalances, subsequently heightening injury susceptibility. Addressi
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Muscle fatigue's indirect link to higher athletic injury risks is a key focus of this study. It highlights how fatigue-induced shifts in muscle resource allocation and movement patterns can lead to biomechanical imbalances, subsequently heightening injury susceptibility. Addressing high injury rates in athletics, this study was conducted in two pivotal phases: the development of a wearable, textile-integrated surface electromyography (sEMG) garment, and the identification of the most effective real-time fatigue metric for true wireless detection for dynamic exercise. While traditional sEMG methods provide valuable insights in laboratory settings, they fall short in dynamically and individually monitoring muscle fatigue in real-world scenarios.
The initial phase focused on creating a smart garment with integrated textile-based electrodes named the RunWave. The second phase concentrated on analyzing muscle fatigue during dynamic running activities, employing an incremental treadmill exercise test. Fatigue was assessed using cardiorespiratory metrics and Borg's Rate of Perceived Exertion (RPE), alongside the evaluation of six fatigue metrics: Average Rectified Value (ARV), approximate and sample entropy, instantaneous mean and median frequencies, and Dimitrov's Spectral Fatigue Index. Significant differences between fatigued and non-fatigued states were observed, especially noted in shifts in entropy, mean and median frequencies, and most prominently in ARV. These findings underscored the necessity for personalized fatigue monitoring strategies, given the variation in fatigue onset and subjective exhaustion experiences among individuals.
The RunWave, with its focus on the ARV metric, emerged as particularly promising for fatigue detection. ARV's computational simplicity and interpretability make it ideal for real-world applications. Despite initial challenges such as fitment issues, electronic limitations, and garment robustness, the RunWave garment was positively received for its comfort and practicality. With targeted improvements, the RunWave garment, leveraging ARV, shows great potential for effectively monitoring muscle fatigue in runners, suggesting a substantial step forward in reducing injury risks in athletic contexts.