http://repositorio.unapec.edu.do/handle/123456789/892
Título : | Breath analyzer for personalized monitoring of exercise-induced metabolic fat burning |
Autor : | Del Orbe, Dionisio V. Park, Hyung Ju Kwack, Myung-Joon Lee, Hyung-Kun Kim, Do Yeob Lim, Jung Gweon Park, Inkyu Sohn, Minji Lim, Soo Lee, Dae-Sik |
Palabras clave : | Breath analyzer Low-power chemiresistive sensor Sensor array Physical exercise Metabolic fat burning monitoring Recurrent neural network |
Fecha de publicación : | oct-2022 |
Editorial : | Sensors and Actuators B: Chemical |
Citación : | Orbe, Dionisio & Park, Hyung & Kwack, Myung-Joon & Lee, Hyung Kun & Kim, Do Yeob & Lim, Jung & Park, Inkyu & Sohn, Minji & Lim, Soo & Le, Dae-Sik. (2022). Breath analyzer for personalized monitoring of exercise-induced metabolic fat burning. Sensors and Actuators B: Chemical. 369. https://doi.org/10.1016/j.snb.2022.132192 |
Resumen : | Obesity increases the risk of chronic diseases, such as type 2 diabetes mellitus, dyslipidemia, and cardiovascular diseases. Simple anthropometric measurements have time limitations in reflecting short-term weight and body fat changes. Thus, for detecting, losing or maintaining weight in short term, it is desirable to develop portable/ compact devices to monitor exercise-induced fat burn in real time. Exhaled breath acetone and blood-borne β-hydroxybutyric acid (BOHB) are both correlated biomarkers of the metabolic fat burning process that takes place in the liver, predominantly post-exercise. Here, we have fabricated a compact breath analyzer for convenient, noninvasive and personalized estimation of fat burning in real time in a highly automated manner. The analyzer collects end-tidal breath in a standardized, user-friendly manner and it is equipped with an array of four low-power MEMS sensors for enhanced accuracy; this device presents a combination of required and desirable design features in modern portable/compact breath analyzers. We analyzed the exhaled breath (with our analyzer) and the blood samples (for BOHB) in 20 participants after exercise; we estimated the values of BOHB, as indication of the fat burn, resulting in Pearson coefficient r between the actual and predicted BOHB of 0.8. The estimation uses the responses from the sensor array in our analyzer and demographic and anthropo- metric information from the participants as inputs to a machine learning algorithm. The system and approach herein may help guide regular exercise for weight loss and its maintenance based on individuals’ own metabolic changes. |
Descripción : | Dionisio V. Del Orbe recibió su Licenciatura en Ingeniería Aeronáutica de la Universidad de Western Michigan (2012), EE. UU., y una Maestría en Ingeniería de Manufactura Microelectrónica del Instituto de Tecnología de Rochester (2015), EE. UU. Recibió su doctorado en Ingeniería Mecánica KAIST (2022), Corea del Sur, y trabajó como investigador de posgrado en el Departamento de Investigación de TIC Médicas y de Bienestar en ETRI, Corea del Sur. Su investigación se centra en sensores de gases químicos para diversas aplicaciones, especialmente, análisis de aliento y detección de gases tóxicos/inflamables; también tiene intereses en dispositivos portátiles y flexibles. Actualmente, es docente e investigador en UNAPEC, República Dominicana. |
URI : | http://repositorio.unapec.edu.do/handle/123456789/892 |
ISSN : | 0925-4005 |
Aparece en las colecciones: | Artículos científicos |
Fichero | Descripción | Tamaño | Formato | |
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Breath analyzer for personalized monitoring of exercise-induced metabolic.pdf | Breath analyzer for personalized monitoring of exercise-induced metabolic | 4.11 MB | Adobe PDF | Visualizar/Abrir |
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