Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156384
Title: Machine learning-reinforced noninvasive biosensors for healthcare
Authors: Zhang, Kaiyi
Wang, Jianwu
Liu, Tianyi
Luo, Yifei
Loh, Xian Jun
Chen, Xiaodong
Keywords: Engineering::Materials
Issue Date: 2021
Source: Zhang, K., Wang, J., Liu, T., Luo, Y., Loh, X. J. & Chen, X. (2021). Machine learning-reinforced noninvasive biosensors for healthcare. Advanced Healthcare Materials, 10(17), 2100734-. https://dx.doi.org/10.1002/adhm.202100734
Project: A18A1b0045
NRF-NRFI2017-07
MOE2019-T2-2-022
Journal: Advanced Healthcare Materials
Abstract: The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.
URI: https://hdl.handle.net/10356/156384
ISSN: 2192-2640
DOI: 10.1002/adhm.202100734
Rights: This is the peer reviewed version of the following article: Zhang, K., Wang, J., Liu, T., Luo, Y., Loh, X. J. & Chen, X. (2021). Machine learning-reinforced noninvasive biosensors for healthcare. Advanced Healthcare Materials, 10(17), 2100734-, which has been published in final form at https://doi.org/10.1002/adhm.202100734. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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