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Smart textiles for multimodal wearable sensing using highly stretchable multiplexed optical fiber system
This paper presents the development and application of a multiparameter, quasi-distributed smart textile based on embedded highly stretchable polymer optical fiber (POF) sensors. The POF is fabricated using the light polymerization spinning process, resulting a highly stretchable optical fiber, so-c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431548/ https://www.ncbi.nlm.nih.gov/pubmed/32807827 http://dx.doi.org/10.1038/s41598-020-70880-8 |
Sumario: | This paper presents the development and application of a multiparameter, quasi-distributed smart textile based on embedded highly stretchable polymer optical fiber (POF) sensors. The POF is fabricated using the light polymerization spinning process, resulting a highly stretchable optical fiber, so-called LPS-POF, with Young’s modulus and elastic limits of 15 MPa and 17%, respectively. The differential scanning calorimetry shows a thermal stability of the LPS-POF in temperature range of 13–40 °C. The developed sensors are based on the optical power variation, which results in a fully portable and low-cost technique. In order to obtain a multiplexed sensor system, a technique based on flexible light emitting diodes (LEDs) on–off keying modulation is applied, where each LED represents the response of one sensor. The smart textile comprises of LPS-POF and three flexible LEDs embedded in neoprene textile fabric. The performance of the system is evaluated for temperature, transverse force and angular displacement detection at different planes. The sensors presented high linearity (mean determination coefficient of 0.99) and high repeatability (inter-measurement deviations below 5%). The sensor is also applied in activity detection, where the principal component analysis (PCA) was applied in the sensors responses and, in conjunction with clustering techniques such as k-means, indicate the possibility of detecting basic activities such as walking, sitting on a chair and squatting. |
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