2022
DOI: 10.1038/s41578-022-00460-x
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End-to-end design of wearable sensors

Abstract: Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their… Show more

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Cited by 593 publications
(387 citation statements)
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“…A growing number of research studies that have been reported on analyzing various sweat biomarkers for use in a broad range of clinical diagnostic purposes, including health concerns, nutritional imbalance, drug abuse, and many more [ 1 , 2 , 3 , 4 ], have seen a ten-fold rise in this field, as shown in Figure 1 . However, there are still gaps in the structured content on related SSD applications based on the current existing review articles, such as insufficient information about a few sweat biomarkers, especially hormones, drugs, nicotine, and vitamin C, and inadequate detailed explanation of the respective application concepts related to these biomarkers and a lack of a fundamental response mechanism of chemical sensors that enable users to understand the process of the target analyte’s detection for various sweat compositions [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Despite these limitations, the existing reviews seemed to disregard addressing issues such as an analysis effect formation mixing new and old sweat samples, which can be a daunting challenge for acquiring a high precision in sweat analysis.…”
Section: Introductionmentioning
confidence: 99%
“…A growing number of research studies that have been reported on analyzing various sweat biomarkers for use in a broad range of clinical diagnostic purposes, including health concerns, nutritional imbalance, drug abuse, and many more [ 1 , 2 , 3 , 4 ], have seen a ten-fold rise in this field, as shown in Figure 1 . However, there are still gaps in the structured content on related SSD applications based on the current existing review articles, such as insufficient information about a few sweat biomarkers, especially hormones, drugs, nicotine, and vitamin C, and inadequate detailed explanation of the respective application concepts related to these biomarkers and a lack of a fundamental response mechanism of chemical sensors that enable users to understand the process of the target analyte’s detection for various sweat compositions [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Despite these limitations, the existing reviews seemed to disregard addressing issues such as an analysis effect formation mixing new and old sweat samples, which can be a daunting challenge for acquiring a high precision in sweat analysis.…”
Section: Introductionmentioning
confidence: 99%
“…Wearable sensors represent a unique class of diagnostic tools for real-time, noninvasive, or minimally invasive investigation of human physiology. Engineering advances in materials, design, electronics, and data analysis form the basis for a rich variety of wearable sensors that conformally mount on soft biological tissues and offer unprecedented insights into various biological processes. , Examples include devices integrated on the skin, eye, and mouth to measure a wide range of biochemical and biophysical parameters. Among these, wearable sweat sensors are of particular interest due to the physiological significance of sweat, ease of sample collection, and availability of a large area for device integration.…”
mentioning
confidence: 99%
“…Unlike conventional diagnostic testing, which takes place at a centralized clinical laboratory, wearable sensors can provide continuous physiological information about the health of an individual 1 . The sensors thus have the potential to personalize the diagnosis of diseases, that is, abnormal conditions could be identified -practically in real time -by measuring deviations (or patterns of deviations) from healthy baselines.…”
mentioning
confidence: 99%