AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918202668220416 |
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| author | Tang, Chenyu Yi, Wentian Zhang, Zibo Occhipinti, Edoardo Occhipinti, Luigi G. |
| author_facet | Tang, Chenyu Yi, Wentian Zhang, Zibo Occhipinti, Edoardo Occhipinti, Luigi G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors Tang, Chenyu Yi, Wentian Zhang, Zibo Occhipinti, Edoardo Occhipinti, Luigi G. Signal Processing 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. |
| title | AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.08500 |