Modeling adaptive programmable smart materials for enhancing athletic performance through biomechanical adaptation and energy optimization

Introduction This study presents an adaptive modeling framework for programmable self-assembled smart materials designed to enhance athletic performance through biomechanical adaptation and energy optimization. Methods The proposed Adaptive Programmable Material Model (APMM) integrates three interconnected modules: the Material Dynamics Module (MDM), the Biomechanical Adaptation Module (BAM), and the Energy Optimization Module (EOM), enabling continuous interaction between material properties, biomechanical feedback, and energy constraints. The framework incorporates the Adaptive Biomechanical Feedback Strategy (ABFS) and the Energy-Constrained Optimization Strategy (ECOS) to support dynamic responsiveness and efficient energy use. Results and Discussion Experimental results across four benchmark datasets show that the proposed method achieves up to 89.78% accuracy and improves energy efficiency by 8.6% compared to state-of-the-art baselines. These findings demonstrate the model’s effectiveness in enhancing biomechanical responsiveness and energy-aware performance, offering a practical foundation for next-generation sports equipment design.