A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917161176399872 |
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| author | Tang, Chenyu Yi, Wentian Xu, Muzi Jin, Yuxuan Zhang, Zibo Chen, Xuhang Liao, Caizhi Smielewski, Peter Occhipinti, Luigi G. |
| author_facet | Tang, Chenyu Yi, Wentian Xu, Muzi Jin, Yuxuan Zhang, Zibo Chen, Xuhang Liao, Caizhi Smielewski, Peter Occhipinti, Luigi G. |
| contents | In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1% to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artefacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation < 10%. Coupled with deep learning, explainable artificial intelligence (XAI), and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00753 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life Tang, Chenyu Yi, Wentian Xu, Muzi Jin, Yuxuan Zhang, Zibo Chen, Xuhang Liao, Caizhi Smielewski, Peter Occhipinti, Luigi G. Signal Processing Artificial Intelligence In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1% to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artefacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation < 10%. Coupled with deep learning, explainable artificial intelligence (XAI), and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management. |
| title | A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2408.00753 |