Hugo Posada-Quintero

Assistant Professor

Department of Biomedical Engineering

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Education

• B.S., Electronic Engineering, Universidad Distrital Francisco José de Caldas, Bogota Colombia, 2005
• M.S., Electronic and Computers Engineering, Universidad de los Andes, Bogota Colombia, 2008
• Ph. D., Biomedical Engineering, University of Connecticut, 2016

Research Focus

My research includes the development of signal processing techniques, wearable instrumentation, and sensors for biomedical applications. Specifically, the aim of my research is to develop models and biomedical instrumentation for the detection and prediction of stress, fatigue, pain, emotional state, hydration status, wakefulness, cognitive performance, heart failure, among others. We use modern mathematical tools to process bioelectrical signals obtained from different sites of the body, like the electrocardiogram, electromyogram, photoplethysmogram, electrodermal activity, and explore the relationship between those signals and the biomedical variable being detected or predicted. Our mathematical processes are focused on the development of more sensitive biomarkers and features, and the development of multimodal algorithms (multiple signals combined). In addition, we use our novel features and train artificial intelligence tools (machine learning and deep learning algorithms) for the development of more accurate models. Furthermore, we develop novel sensors and electronic devices to better capture the electrophysiological signals using portable and wearable devices.

Publications

Hugo F. Posada-Quintero, Carol S. Landon, Nicole M. Stavitzski, Jay B. Dean, and Ki H. Chon. “Seizures Caused by Exposure to Hyperbaric Oxygen in Rats Can Be Predicted by Early Changes in Electrodermal Activity.” Frontiers in Physiology 12 (2022): 2319.

Newlin Lew, Kelley, Tracey Arnold, Catherine Cantelmo, Francky Jacque, Hugo F. Posada-Quintero, Pooja Luthra, and Ki H. Chon. “Diabetes Distal Peripheral Neuropathy: Subtypes and Diagnostic and Screening Technologies.” Journal of Diabetes Science and Technology, January 7, 2022, 19322968211035376.

Hossain, Md-Billal, Hugo F. Posada-Quintero, Youngsun Kong, Riley McNaboe, and Ki H. Chon. “Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning.” Biomedical Signal Processing and Control 74 (April 1, 2022): 103483.

Hernando, Alberto, Hugo F. Posada-Quintero, María Dolores Peláez-Coca, Eduardo Gil, and Ki H. Chon. “Autonomic Nervous System Characterization in Hyperbaric Environments Considering Respiratory Component and Non-Linear Analysis of Heart Rate Variability.”
Computer Methods and Programs in Biomedicine 214 (February 1, 2022): 106527.

Kong, Youngsun, Hugo F. Posada-Quintero, David Gever, Lia Bonacci, Ki H. Chon, and Jeffrey Bolkhovsky. “Multi-Attribute Task Battery Configuration to Effectively Assess Pilot Performance Deterioration during Prolonged Wakefulness.” Informatics in Medicine Unlocked
28 (January 1, 2022): 100822.

Hugo F. Posada-Quintero, Youngsun Kong, and Ki H. Chon. “Objective Pain Stimulation Intensity and Pain Sensation Assessment Using Machine Learning Classification and Regression Based on Electrodermal Activity.” American Journal of Physiology-Regulatory,
Integrative and Comparative Physiology 321, no. 2 (August 1, 2021): R186–96.

Kong, Youngsun, Hugo F. Posada-Quintero, and Ki H. Chon. “Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor.” Sensors 21, no. 12 (January 2021): 3956.