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An ultralight, flexible, and biocompatible all-fiber motion sensor for artificial intelligence wearable electronics
Open accessAbstract
Abstract New-generation human body motion sensors for wearable electronics and intelligent medicine are required to comply with stringent requirements in terms of ultralight weight, flexibility, stability, biocompatibility, and extreme precision. However, conventional sensors are hard to fulfill all these criteria due to their rigid structure, high-density sensing materials used as the constituents, as well as hermetical and compact assembly strategy. Here, we report an ultralight sensing material based on radial anisotropic porous silver fiber (RAPSF), which has been manufactured by phase separation and temperature-controlled grain growth strategy on a modified blow-spinning system. The resistance of RAPSF could be dynamically adjusted depending on the deflected shape. Furthermore, an all-fiber motion sensor (AFMS) with an ultra-low density of 68.70 mg cm −3 and an overall weigh of 7.95 mg was fabricated via layer-by-layer assembly. The sensor exhibited outstanding flexibility, breathability, biocompatibility, and remarkable body motion recognition ability. Moreover, the AFMS was shown to have great potential as an artificial intelligence throat sensor for throat state identification at the accuracy above 85%, allowing one to spot the early onset of the viral throat illness.
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Lin et al. employed an ultralight sensing material based on radial anisotropic porous silver fiber to collect four types of throat motion signals, including two speaking signals ("Goodbye" and "Hello"), swallowing, and coughing.
“Lin et al. [222] employed an ultralight sensing material based on radial anisotropic porous silver fiber to collect four types of throat motion signals, including two speaking signals (“Goodbye” and “Hello”), swallowing, and coughing.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [222]
AI helped sensors with throat state identification at an accuracy above 85% and the early diagnosis of viral throat illness.
“The AI helped sensors with throat state identification at an accuracy above 85% and the early diagnosis of viral throat illness.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [222]
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Metadata
- Authors
- Lin, S., Hu, S., Song, W., Gu, M., Liu, J., Song, J., Liu, Z., Li, Z., Huang, K., Wu, Y., Lei, M., & Wu, H.
- Affiliations
- Guangxi University (CN)
- Beijing University of Posts and Telecommunications (CN)
- State Key Laboratory of New Ceramics and Fine Processing (CN)
- Corresponding author
- Lin, Sen
- Lei, Ming
- Wu, Hui
- ORCID
- Venue
- npj Flexible Electronics
- Published
- 2022-04-28
- Volume
- 6
- Issue
- 1
- ISSN
- 2397-4621 (ISSN-L)
- OpenAlex
- W4293180449
- Cited by
- 71
- References
- 45
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 19 · 2023: 15 · 2022: 7
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Flexible Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Army Medical University
- Fundamental Research Funds for the Central Universities (2021XD‐A041)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Nanomaterials and Printing Technologies
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wearable electronics, flexible electronics, biocompatibility, phase separation, blow spinning
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