An Advanced LSTM Model to Enhance Running Economy

Abstract

Running Economy (RE) is an important physiological measure for endurance athletes, particularly distance runners. It is defined as the energy demand for a specific velocity of submaximal running and depends on biomechanical, metabolic, cardiorespiratory, and neuromuscular factors. In this work, we introduce a novel approach for predicting running economy in amateur runners through the analysis and comparison of three neural network architectures: a proposed Long Short-Term Memory (LSTM) model, an alternative LSTM model, and a Recurrent Neural Network (RNN). Each model incorporates physiological and biomechanical metrics, including pace, heart rate, power, cadence, ground contact time, and stride length, collected with a wearable device.