High‐performance piezoelectric yarns for artificial intelligence‐enabled wearable sensing and classification
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Abstract Piezoelectric polymer fibers offer a fundamental element in intelligent fabrics with their shape adaptability and energy‐conversion capability for wearable activity and health monitoring applications. Nonetheless, realizing high‐performance smart polymer fibers faces a technical challenge due to the relatively low piezoelectric performance. Here, we demonstrate high‐performance piezoelectric yarns simultaneously equipped with structural robustness and mechanical flexibility. The key to substantially enhanced piezoelectric performance is promoting the electroactive β‐phase formation during electrospinning via adding an adequate amount of barium titanate (BaTiO 3 ) nanoparticles into the poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE)). When transformed into a yarn structure by twisting the electrospun mats, the BaTiO 3 ‐doped P(VDF‐TrFE) fibers become mechanically strengthened with significantly improved elastic modulus and ductility. Owing to the tailored convolution neural network algorithms architected for classification, the as‐developed BaTiO 3 ‐doped piezo‐yarn device woven into a cotton fabric exhibits monitoring and identifying capabilities for body signals during seven human motion activities with a high accuracy of 99.6%. image
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A CNN with a multi-kernel efficiently classified motion signals captured from the body through piezo-yarns, yielding an accuracy of 99.6%.
“Therefore, to better identify human activities, a CNN with a multi-kernel was considered to efficiently classify the motion signals captured from the body through piezo-yarns, yielding an accuracy of 99.6%.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [223]
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The emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535 · see citing works
- Authors
- Kim, D., Yang, Z., Cho, J., Park, D., Kim, D. H., Lee, J., Ryu, S., Kim, S., & Kim, M.
- Affiliations
- Sungkyunkwan University (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Yonsei University (KR)
- Corresponding author
- Kim, Sang‐Woo
- Kim, Miso
- Venue
- EcoMat
- Publisher
- Wiley
- Published
- 2023-06-13
- Volume
- 5
- Issue
- 8
- ISSN
- 2567-3173 (ISSN-L)
- OpenAlex
- W4380538238
- Cited by
- 64
- References
- 59
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 23 · 2023: 7
- FWCI
- 5.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EcoMat (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National NanoFab Center
- National Research Foundation of Korea (NRF‐2018M3D1A1058794, NRF‐2021M3A7C2089759, NRF‐2021R1A2C2095767)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Dielectric materials and actuators
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- P(VDF-TrFE), BaTiO3 nanoparticles, β-phase formation, electrospinning, smart textiles, wearable sensors, convolutional neural networks, mechanical flexibility, health monitoring
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