{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T01:18:35Z","timestamp":1773451115178,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Texas STARs","award":["Award# P5011032"],"award-info":[{"award-number":["Award# P5011032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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 \u00b120% 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.<\/jats:p>","DOI":"10.3390\/s24134233","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Wearable Alcohol Monitoring Device for the Data-Driven Transcutaneous Alcohol Diffusion Model"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1857-1621","authenticated-orcid":false,"given":"Ahmed Hasnain","family":"Jalal","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sepehr","family":"Arbabi","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, University of Texas Permian Basin, Odessa, TX 79762, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad A.","family":"Ahad","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Georgia Southern University, Statesboro, GA 30458, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahmida","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Ashfaq","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Baptist Health South Florida, Miami, FL 33186, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/BF03160911","article-title":"The clinical significance of variations in ethanol toxicokinetics","volume":"3","author":"Pizon","year":"2007","journal-title":"J. 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