Abstract

Wearable biosensors are garnering substantial interest due to their potential to provide continuous, real-time physiological information via dynamic, noninvasive measurements of biochemical markers in biofluids, such as sweat, tears, saliva and interstitial fluid. Recent developments have focused on electrochemical and optical biosensors, together with advances in the noninvasive monitoring of biomarkers including metabolites, bacteria and hormones. A combination of multiplexed biosensing, microfluidic sampling and transport systems have been integrated, miniaturized and combined with flexible materials for improved wearability and ease of operation. Although wearable biosensors hold promise, a better understanding of the correlations between analyte concentrations in the blood and noninvasive biofluids is needed to improve reliability. An expanded set of on-body bioaffinity assays and more sensing strategies are needed to make more biomarkers accessible to monitoring. Large-cohort validation studies of wearable biosensor performance will be needed to underpin clinical acceptance. Accurate and reliable real-time sensing of physiological information using wearable biosensor technologies would have a broad impact on our daily lives.

Keywords

BiosensorWearable computerComputer scienceNanotechnologyEmbedded systemMaterials science

MeSH Terms

Biosensing TechniquesBiotechnologyBody FluidsComputer SystemsHumansIontophoresisLab-On-A-Chip DevicesMonitoringPhysiologicSalivaSweatTearsWearable Electronic Devices

Affiliated Institutions

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Publication Info

Year
2019
Type
review
Volume
37
Issue
4
Pages
389-406
Citations
2946
Access
Closed

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Social media, news, blog, policy document mentions

Citation Metrics

2946
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27
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Cite This

Jayoung Kim, Alan S. Campbell, Berta Esteban‐Fernández de Ávila et al. (2019). Wearable biosensors for healthcare monitoring. Nature Biotechnology , 37 (4) , 389-406. https://doi.org/10.1038/s41587-019-0045-y

Identifiers

DOI
10.1038/s41587-019-0045-y
PMID
30804534
PMCID
PMC8183422

Data Quality

Data completeness: 86%