Integrating footwear features into fatigue prediction models for marathon runners: A hybrid CNN-LSTM approach

Footwear design, especially the curvature of carbon plates, may influence fatigue perception, but few studies have integrated footwear features into fatigue prediction models. This study aimed to develop a hybrid CNN-LSTM model to predict runners’ fatigue states and evaluate the impact of footwear characteristics on fatigue perception. Twelve male marathon runners (age = 21.8 ± 1.3 years; body mass = 59.1 ± 4.1 kg; height = 168.9 ± 2.2 cm; and weekly mileage = 68.8 ± 5.5 km) participated. They wore two types of carbon-plated shoes (flat plate, FP, and curved plate (CP)) and ran at a steady pace (Borg score 13) until a Borg score of 16 or 85% of maximum heart rate was reached for 2 min. EMG signals and physiological data were collected during treadmill running. A hybrid CNN-LSTM model was trained with and without footwear features to predict fatigue states. The model with footwear features achieved 85% accuracy, compared to 69% without. Curved carbon plate (CP) shoes delayed semi-fatigue onset, indicating better initial support, but the time to full fatigue was similar for both shoe types. The CNN-LSTM model effectively predicted fatigue states, with significant improvement when footwear features were included. Footwear design, particularly carbon plate curvature, influenced fatigue perception.