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.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.