## 1 Introduction  
Measuring the metabolic cost from biomechanical data, like ground reaction forces and joint moments, is vital for enhancing health, rehabilitation, and sports through human movement science [1]. Traditional methods need up to five minutes to average metabolic cost data, but recent advances have cut this to about two minutes [2]. However, these techniques still cannot quickly assess metabolic costs during a gait cycle, posing challenges for patients in lengthy walking tests. The significance of ground reaction forces (GRFs) in biomechanical analysis is well-documented, with methodologies ranging from direct force plate measurements to sophisticated estimations using wearable sensors and inertial motion capture systems [3]. Such advancements have democratized gait and movement analysis, extending the reach of biomechanical assessments beyond the confines of specialized laboratories [4]. Similarly, joint moments offer a window into the internal dynamics of the musculoskeletal system, revealing the intricate interplay between muscle forces and the resulting movements [4; 5]. Recent research has further pushed the boundaries of biomechanical analysis by integrating these metrics with advanced computational models to predict metabolic cost [5; 6; 7; 8; 9]. This interdisciplinary approach melts the precision of biomechanical measurements with the predictive power of computational algorithms, unveiling new perspectives on the energetics of human movement. The use of neural networks, in particular, exemplifies this trend, showcasing the potential of machine learning in deciphering complex relationships between biomechanical inputs and metabolic outcomes [10]. Drawing from a broader spectrum of artificial intelligence (AI) applications, our previous work has demonstrated the efficacy of optimization methods and artificial neural networks (ANNs) across diverse domains [11; 12; 13]. These studies underscore AI techniques' adaptability and bring novel insights into biomechanical analysis. A unified model was developed that leverages walking mechanics to estimate metabolic costs under various conditions, demonstrating the potential of using biomechanical measurements, such as the GRF and joint moments, to predict metabolic costs in human walking [1]. The model's capability to accommodate different walking conditions underscores its versatility and applicability in biomechanical analysis, offering new insights into the energetic efficiency of gait. Another study [14] compared metabolic cost calculations in gait using musculoskeletal energy models. They evaluated the correlation of several metabolic energy expenditure models with experimental data, enriching our understanding of how different models predict the metabolic costs of gait under varied conditions. Integrating the GRF, marker data, and pulmonary gas exchange measurements in their methodology provides a comprehensive approach to assessing the metabolic implications of gait dynamics. By synthesizing insights from multiple studies, our research underscores the value of merging biomechanical data with AI models to predict metabolic costs. It leverages AI to evaluate how the GRF and joint moments data forecast metabolic costs, enhancing our theoretical and practical understanding of biomechanical energetics. This work paves the way for developing interventions and devices to improve movement efficiency and health. ANNs are computational models inspired by the human brain's structure and function [15; 16]. Recent research has leveraged ANNs to gain deeper insights into the mechanics of movement and the prediction of metabolic costs, offering substantial advancements in personalized healthcare and athletic performance optimization. For instance, a literature review [17] demonstrated the application of ANNs in predicting the metabolic cost associated with different physical activities, showcasing the potential of machine learning in enhancing our understanding of energy expenditure in human motion.