The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Decoding lip language using triboelectric sensors with deep learning
Open accessAbstract
Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we propose the concept of a novel lip-language decoding system with self-powered, low-cost, contact and flexible triboelectric sensors and a well-trained dilated recurrent neural network model based on prototype learning. The structural principle and electrical properties of the flexible sensors are measured and analysed. Lip motions for selected vowels, words, phrases, silent speech and voice speech are collected and compared. The prototype learning model reaches a test accuracy of 94.5% in training 20 classes with 100 samples each. The applications, such as identity recognition to unlock a gate, directional control of a toy car and lip-motion to speech conversion, work well and demonstrate great feasibility and potential. Our work presents a promising way to help people lacking a voice live a convenient life with barrier-free communication and boost their happiness, enriches the diversity of lip-language translation systems and will have potential value in many applications.
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Self-citationOn the origin of contact-electrification
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Self-citationSkin-inspired highly stretchable and conformable matrix networks for multifunctional sensing
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Cited by
Hegde, C., Su, J., Tan, J. M. R., He, K., Chen, X., & Magdassi, S. (2023) · ACS Nano · doi:10.1021/acsnano.3c04089 · Cited by 384
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Toward an AI Era: Advances in Electronic Skins
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Metadata
- Authors
- Lu, Y., Tian, H., Cheng, J., Zhu, F., Liu, B., Wei, S., Ji, L., & Wang, Z. L.
- Venue
- Nature Communications
- Published
- 2022-03-17
- Volume
- 13
- Issue
- 1
- Pages
- 1401-1401
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4220822444
- Cited by
- 253
- References
- 43
- FWCI
- 14.78
- Type
- journal article
- Language
- en
- Open access
- gold
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