Our Faculty, Staff & Students

Contact Us

Professor and Department Head

Dr. Ki Chon
Phone: (860) 486-4767
Email: kchon@uconn.edu


Lisa Ephraim
Undergraduate Academic Advisor
Phone: (860) 486-0163
E-mail: lisae@engr.uconn.edu

Jennifer Seyford
Administrative Program Support III
Phone: (860) 486-0116
E-mail: jennifer.seyford@uconn.edu

Birgit Sawstrom
Administrative Program Support II
Phone: (860) 486-1267 
E-mail: birgit.sawstrom@uconn.edu

Sowmya Ramesh
Financial Assistant I
Phone: (860) 486-7139
E-mail: sowmya.ramesh@uconn.edu

Sarah Dunnack
Administrative Program Support I
Phone: (860) 486-5838
E-mail: sarah.dunnack@uconn.edu



Main Office Address

Biomedical Engineering Department
A.B. Bronwell Building, Room 217
260 Glenbrook Road, Unit 3247
University of Connecticut
Storrs, CT 06269-3247
Phone: (860) 486-5838
Fax: (860) 486-2500

Posada-Quintero, Hugo

Hugo Posada-Quintero, Ph.D.
Assistant Professor
Research Interests:  
Biomedical instrumentation, biomedical signal processing, human emotions, biosensors
260 Glenbrook Road, Unit 3247
A.B. Bronwell, Room 108
University of Connecticut
Storrs, CT 06269-3247
Office Phone: 860-486-5099
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 Summary:
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.

Selected Publications:

Google Scholar Citations List

  1. Posada-Quintero, Hugo F., and Ki H. Chon. Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review. Sensors 20, no. 2 (January 2020): 479. 
  2. Posada-Quintero, Hugo F., John P Florian, Alvaro D Orjuela-Cañón, Tomas Aljama-Corrales, Sonia Charleston-Villalobos, Ki H Chon. ‘Power Spectral Density Analysis of Electrodermal Activity for Sympathetic Function Assessment’, Annals of Biomedical Engineering, 44(10), 3124-3135, 2016. 
  3. Posada-Quintero, H. F., Florian, J. P., Orjuela-Cañón, Á. D., & Chon, K. H. ‘Highly sensitive index of sympathetic activity based on time-frequency spectral analysis of electrodermal activity’. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 311(3), R582–R591, 2016. 
  4. Posada-Quintero, H. F., & Bolkhovsky, J. B. (2019). Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity. Behavioral Sciences, 9(4), 45.  
  5. Posada-Quintero, Hugo F., D Delisle-Rodríguez, MB Cuadra-Sanz, RRF de la Vara-Prieto. ‘Evaluation of pulse rate variability obtained by the pulse onsets of the photoplethysmographic signal’ Physiological Measurement 34 (2), 179, 2013.