AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors

作者：Chenyu Tang, Wentian Yi, Zibo Zhang, Edoardo Occhipinti, Luigi G. Occhipinti · 发表于：IEEE Transactions on Biomedical Engineering · 年份：2025 · DOI：10.1109/tbme.2025.3588051 · 被引用次数：7 · 研究领域：E-commerce and Technology Innovations、Advanced Sensor and Energy Harvesting Materials、Non-Invasive Vital Sign Monitoring

Wearable biosensors have revolutionized human performance monitoring by enabling real-time assessment of physiological and biomechanical parameters. However, existing solutions lack the ability to simultaneously capture breath-force coordination and muscle activation symmetry in a seamless and non-invasive manner, limiting their applicability in strength training and rehabilitation. This work presents a wearable smart sportswear system that integrates screen-printed graphene-based strain sensors with compact electronics for wireless data transfer and a deep learning framework for real-time classification of exercise execution quality. By leveraging 1D ResNet-18 for feature extraction, the system achieves 92.1% classification accuracy across six exercise conditions, distinguishing between breathing irregularities and asymmetric muscle exertion. Additionally, t-SNE analysis and Grad-CAM-based explainability visualization confirm that the network accurately captures biomechanically relevant features, ensuring robust interpretability. The proposed system establishes a foundation for next-generation AI-powered sportswear, with applications in fitness optimization, injury prevention, and adaptive rehabilitation training.