PDF(Pubmed)
Abstract：
Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers\' wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration, Cmax, in the stratum corneum from the transient analysis. Lastly, we found that a more significant time difference between BACmax and Cmax was due to greater alcohol consumption for a fixed absorption time.
摘要：
可穿戴酒精监测设备要求非侵入性，血液酒精含量（BAC）的实时测量可靠和连续。一些商业设备可用于通过检测经皮扩散的酒精来无创地确定BAC。然而,由于人体皮肤经皮酒精扩散的复杂情况和许多因素（例如，蒙皮厚度,酒精动力学，体重,年龄,性别,代谢率，等。).在这项工作中,一个经皮酒精扩散模型已经从实时捕获的数据从人类手腕开发，以更好地了解从血液扩散到不同的皮肤表皮层的酒精动力学。这样的模型将是在较大的研究中确定基础计算模型的足迹。八名匿名志愿者参与了这项试点研究。实验室构建的可穿戴血液酒精含量（BAC）监测设备收集了所有数据以开发此扩散模型。质子交换膜燃料电池（PEMFC）传感器与nRF51822微控制器集成，LMP91000小型化恒电位仪，2.4GHz收发器，支持蓝牙低功耗(BLE)，和所有必要的电子元件来构建这个可穿戴BAC监测设备。使用此设备从这些志愿者的手腕收集实时的%BAC数据，并存储在终端设备中(例如，智能手机)。从捕获的数据中，通过比较初始阶段（=10分钟）和后期（=100分钟）的酒精浓度，我们证明了皮肤上的挥发性酒精浓度如何随时间变化。我们还将实验结果与三种不同输入曲线的输出进行比较：分段线性，指数线性，和Hoerl,优化开发的扩散模型。我们的结果表明，与分段线性和Hoerl函数相比，指数线性函数最适合实验数据。此外，我们研究了皮肤表皮厚度在±20％以内的影响，并证明与较厚的皮肤相比，该厚度减少20％会导致更快的动力学。该模型清楚地显示了皮肤表皮层内的扩散前沿如何随时间变化。我们进一步验证了60分钟大致是达到最大浓度的时间，Cmax,从瞬时分析的角质层。最后,我们发现,BACmax和Cmax之间存在更显著的时间差是由于在固定的吸收时间内饮酒量增加.
