Running is one of the most practiced sports worldwide, offering numerous health benefits, but also carrying a risk of injury, mainly at the knee and ankle joints. The origin of running injuries is not fully understood. With predictive neuromusculoskeletal simulations, more insight could be gained into the biomechanical mechanisms that may lead to injuries.
However, in predictive simulations of gait, hyperextension of the knee during stance phase is often encountered. This limits their applicability in research into running-related injuries. It is unclear what causes these unrealistic kinematics, with various studies coming to conflicting conclusions.
This study aims to identify the cause of knee hyperextension in predictive models of running and subsequently, to determine the essential modeling elements for accurately simulating stance knee flexion.

A structured analysis was conducted to investigate the potential impact of the model components within the predictive simulation framework. This framework was divided into four main categories: the objective function, the musculoskeletal (MSK) model, the foot contact model, and the controller. The analysis resulted in numerous hypotheses regarding the element that might be responsible for the simulation of realistic knee kinematics. SCONE, an open-source package for neuromusculoskeletal predictive simulation, was used to test the effect of each hypothesis on the simulated running kinematics. The simulation outcomes were compared to experimental data to assess possible improvements.

The results demonstrate that, in contrast to previous literature, adaptations to the objective function, the MSK model, and the foot contact model have negligible effects on predicted running kinematics. This leads to the conclusion that the controller is essential to focus on when improving knee kinematics. Due to time constraints, multiphase control could not be implemented. Therefore, the exact reflex pathways and phase transitions should be further investigated for the predictive simulation of running before implementation is possible.

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.