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.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funding Scheme
Grant Reference no. : A18A1b0045
This research / project is supported by the National Research Foundation - NRF Investigatorship
Grant Reference no. : NRF-NRFI2017-07
This research / project is supported by the Singapore Ministry of Education - MOE Academic Research Fund Tier 2
Grant Reference no. : MOE2019-T2-2-022
Description:
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. doi:10.1002/adhm.202100734
, which has been published in final form at http://dx.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