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arXiv · 2504.08500

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

Chenyu Tang·Wentian Yi·Zibo Zhang·Edoardo Occhipinti·Luigi G. Occhipinti
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Abstract

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.

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Chenyu Tang, Wentian Yi, Zibo Zhang, Edoardo Occhipinti, Luigi G. Occhipinti. 2025-04-11. AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors. https://doi.org/10.1109/tbme.2025.3588051

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