Research on optimization and improvement of sports fatigue training based on biomechanical principles

Sports fatigue represents a very important obstacle in athletic performance and it creates the movement inefficiencies, increased injury risk and longer recovery time. It puts forth an integrated fatigue monitoring framework using a biomechanical assessment, a physiological monitoring and a predictive modelling for optimizing fatigue management and training adaptations. The specific techniques utilized to quantify fatigue induced changes in movement efficiency, neuromuscular coordination, autonomic activity are 3D Motion Analysis Systems, Heart Rate Variability (HRV) monitoring, and Infrared Thermography (IRT). Using Bayesian inference, ARIMA time series forecasting and Dynamic Time Warping (DTW) analysis, fatigue thresholds are predicted to enable personalized fatigue management strategies. Throughout all experiments, fatigue led to a 10% decrease in stride length, a 15% increase in ground contact time and a reduction of 20% parasympathetic activity of the HRV, which coincides with a decreased biomechanical efficiency and autonomic system dysregulation. ARIMA predicts short term fatigue cycle with 91%, and Bayesian model estimates individual fatigue thresholds with 95% confidence (Table 1). IRT analysis also shows a fatigued muscle temperature increase of 1.15C, which corroborates on thermal regulation monitoring of fatigue. Moreover, the DTW analysis shows up to 9% deviations in the movement patterns during fatigued conditions, which calls for real time fatigued tracking. These results verify that the combination of real-time biomechanical tracking with predictive analytics offers a more effective, safer and more fatigue resistance way of endurance training. The proposed framework provides an effective data driven approach to real time fatigue monitoring and has practical utilizations in the sports training, injury prevention, and athletic performance optimization.