The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor
Open accessAbstract
Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low‐cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high‐fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal‐to‐noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa−1. With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built‐in algorithm is developed for one‐click health data sharing and data‐driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things.
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Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association
Benjamin, E. J., Muntner, P., Alonso, A., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Das, S. R., et al. (2019) · Circulation · doi:10.1161/cir.0000000000000659 · Cited by 9,036
Pursuing prosthetic electronic skin
Chortos, A., Liu, J., & Bao, Z. (2016) · Nature Materials · doi:10.1038/nmat4671 · Cited by 2,396
Schwartz, G., Tee, B. C.-K., Mei, J., Appleton, A. L., Kim, D. H., Wang, H., & Bao, Z. (2013) · Nature Communications · doi:10.1038/ncomms2832 · Cited by 2,042
Quantifying the triboelectric series
Zou, H., Zhang, Y., Guo, L., Wang, P., He, X., Dai, G., Zheng, H., Chen, C., Wang, A. C., Xu, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09461-x · Cited by 1,920
A flexible and highly sensitive strain-gauge sensor using reversible interlocking of nanofibres
Pang, C., Lee, G.-Y., Kim, T.-I., Kim, S. M., Kim, H. N., Ahn, S.-H., & Suh, K.-Y. (2012) · Nature Materials · doi:10.1038/nmat3380 · Cited by 1,695
A bioinspired flexible organic artificial afferent nerve
Kim, Y., Chortos, A., Xu, W., Liu, Y., Oh, J. Y., Son, D., Kang, J., Foudeh, A. M., Zhu, C., Lee, Y., et al. (2018) · Science · doi:10.1126/science.aao0098 · Cited by 1,466
Boutry, C. M., Negre, M., Jorda, M., Vardoulis, O., Chortos, A., Khatib, O., & Bao, Z. (2018) · Science Robotics · doi:10.1126/scirobotics.aau6914 · Cited by 921
Monitoring of the central blood pressure waveform via a conformal ultrasonic device
Wang, C., Li, X., Hu, H., Zhang, L., Huang, Z., Lin, M., Zhang, Z., Yin, Z., Huang, B., Gong, H., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0287-x · Cited by 895
Triboelectric Nanogenerator (TENG)—Sparking an Energy and Sensor Revolution
Wang, Z. L. (2020) · Advanced Energy Materials · doi:10.1002/aenm.202000137 · Cited by 870
Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays
Zhou, Z., Chen, K., Li, X., Zhang, S., Wu, Y., Zhou, Y., Meng, K., Sun, C., He, Q., Fan, W., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-0428-6 · Cited by 841
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Cited by
Sun, T., Feng, B., Huo, J., Xiao, Y., Wang, W., Peng, J., Li, Z., Du, C., Wang, W., Zou, G., et al. (2024) · Nano-Micro Letters · doi:10.1007/s40820-023-01235-x · Cited by 384
Artificial intelligence-powered electronic skin
Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259
Toward an AI Era: Advances in Electronic Skins
Fu, X., Cheng, W., Wan, G., Yang, Z., & Tee, B. C. K. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00049 · Cited by 172
Flexible Metasurfaces for Multifunctional Interfaces
Zhou, Y., Wang, S., Yin, J., Wang, J., Manshaii, F., Xiao, X., Zhang, T., Bao, H., Jiang, S., & Chen, J. (2024) · ACS Nano · doi:10.1021/acsnano.3c09310 · Cited by 115
Self-citationA Soft Magnetoelastic Generator for Wind‐Energy Harvesting
Zhao, X., Nashalian, A., Ock, I. W., Popoli, S., Xu, J., Yin, J., Tat, T., Libanori, A., Chen, G., Zhou, Y., et al. (2022) · Advanced Materials · doi:10.1002/adma.202204238 · Cited by 109
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Metadata
- Authors
- Fang, Y., Zou, Y., Xu, J., Chen, G., Zhou, Y., Deng, W., Zhao, X., Roustaei, M., Hsiai, T. K., & Chen, J.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2021-08-31
- Volume
- 33
- Issue
- 41
- Pages
- e2104178-e2104178
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3197429751
- Cited by
- 380
- References
- 47
- FWCI
- 18.55
- Type
- journal article
- Language
- en
- Open access
- green
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