Wearable multifunctional sweat-sensing system for efficient healthcare monitoring

可穿戴计算机 生物相容性 计算机科学 流体学 可穿戴技术 纳米技术 生物医学工程 材料科学 嵌入式系统 工程类 医学 电气工程 冶金
作者
Francesca Criscuolo,Ivan Ny Hanitra,Simone Aiassa,Irene Taurino,Nicolò Oliva,Sandro Carrara,Giovanni De Micheli
出处
期刊:Sensors and Actuators B-chemical [Elsevier BV]
卷期号:328: 129017-129017 被引量:65
标识
DOI:10.1016/j.snb.2020.129017
摘要

Despite the huge expansion in recent years of sweat sensing and wearable technologies, several challenges are still open, including poor sample collection, separate sampling and analysis, low multi-sensing capabilities and materials toxicity. In this work, we propose a novel wearable multi-electrode platform efficiently tackling some of these issues. The sensing technology is based on one-step electrodeposited platinum nanostructures to achieve reproducibility and biocompatibility. The platform is highly flexible and includes four electrodes for the simultaneous sensing of analytes, a temperature sensor and a stable reference electrode (RE) with an ionic-liquid junction. A low-cost cotton fluidics is designed to continuously bring fresh sweat to the sensing area, while disposing the already-tested sample. The excellent analytical performance of the proposed technology is proved for different applications: Li+ for Therapeutic Drug Monitoring (TDM) in psychiatric disorders, Pb2+ for the control of heavy metal contamination, K+ and Na+ for sport tracking. The sensors offer linear responses in artificial sweat in the ranges of clinical interest. A simulated wearable setup on a mannequin is used to test reversibility and selectivity. Finally, potassium and sodium are successfully tracked on five human volunteers during physical exercise. The accuracy of the in-situ measurements is demonstrated (Pearson coefficients of 0.97 and 0.81 for Na+ and K+, respectively). With its high biocompatibility, selectivity and accurate sample-handling, this wearable platform represents an important step towards the development of non-invasive monitoring devices for m-Health, paving the way for a better understanding of physiological parameters and clinical needs of each individual.
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