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      Nanomaterials-patterned flexible electrodes for wearable health monitoring: a review

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      1 , 2 , 1 , 3 ,
      Journal of Materials Science
      Springer US

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          Abstract

          Abstract

          Electrodes fabricated on a flexible substrate are a revolutionary development in wearable health monitoring due to their lightweight, breathability, comfort, and flexibility to conform to the curvilinear body shape. Different metallic thin-film and plastic-based substrates lack comfort for long-term monitoring applications. However, the insulating nature of different polymer, fiber, and textile substrates requires the deposition of conductive materials to render interactive functionality to substrates. Besides, the high porosity and flexibility of fiber and textile substrates pose a great challenge for the homogenous deposition of active materials. Printing is an excellent process to produce a flexible conductive textile electrode for wearable health monitoring applications due to its low cost and scalability. This article critically reviews the current state of the art of different textile architectures as a substrate for the deposition of conductive nanomaterials. Furthermore, recent progress in various printing processes of nanomaterials, challenges of printing nanomaterials on textiles, and their health monitoring applications are described systematically.

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          Wearable biosensors for healthcare monitoring

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            Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review

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              Real-time tracking of self-reported symptoms to predict potential COVID-19

              A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.
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                Author and article information

                Contributors
                mhossai5@ncsu.edu
                Journal
                J Mater Sci
                J Mater Sci
                Journal of Materials Science
                Springer US (New York )
                0022-2461
                1573-4803
                28 June 2021
                : 1-43
                Affiliations
                [1 ]GRID grid.443078.c, ISNI 0000 0004 0371 4228, Department of Textile Engineering, , Khulna University of Engineering & Technology, ; Khulna, 9203 Bangladesh
                [2 ]GRID grid.18376.3b, ISNI 0000 0001 0723 2427, UNAM – National Nanotechnology Research Center and, Institute of Materials Science and Nanotechnology, , Bilkent University, ; Ankara, 06800 Turkey
                [3 ]GRID grid.40803.3f, ISNI 0000 0001 2173 6074, Department of Textile Engineering, Chemistry and Science, , North Carolina State University, ; Raleigh, 27606 USA
                Author notes

                Handling Editor: Maude Jimenez.

                Author information
                http://orcid.org/0000-0001-8416-0631
                Article
                6248
                10.1007/s10853-021-06248-8
                8237560
                34219807
                b4512c14-bd9d-402d-8c5b-a5398a686915
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 7 January 2021
                : 8 June 2021
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                Review

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