Human Bio-behavioral Signals for the Development of Novel Real-life Applications
Department of Computer Science & Engineering Department, Texas A&M University
Location: Rudder Tower - Room 410
Time: April 23, 2018 - 2:00-3:00pm
Bio-behavioral signal processing and systems modeling enable an integrated computational approach to the study of human behavior through overt behavioral signals information and covert biomarkers. Recent converging advances in sensing and computing, including wearable technologies, allow the unobtrusive long-term tracking of individuals yielding rich multimodal signal measurements from real-life. In this talk, we will present the development of data-scientific and context-rich bio-behavioral approaches for analyzing, quantifying, and interpreting these bio-behavioral signals. The first part of the talk will describe a novel knowledge-driven signal representation framework able to efficiently handle the large volume of acquired data and the noisy signal measurements. Our approach involves the use of sparse approximation techniques and the design of signal-specific dictionaries learned through Bayesian methods, outperforming previously proposed models in terms of signal reconstruction and information retrieval criteria. The second part will focus on translating the derived signal representations into novel intuitive quantitative measures analyzed with probabilistic and statistical models in relation to external factors of observable behavior. This work has found applications in the family studies domain for identifying instances of emotional escalation and interpersonal conflict, in physical health for continuous glucose monitoring of patients with diabetes, as well as in human-robot interaction applications. The final part of the talk will discuss how the results from this analysis can be employed toward designing human-assistive personalized bio-feedback systems able to promote healthy routines, increase emotional wellness and awareness, and revolutionize clinical assessment and intervention.
Theodora Chaspari is an Assistant Professor at the Computer Science & Engineering Department in Texas A&M University. She has received the diploma (2010) in Electrical and Computer Engineering from the National Technical University of Athens, Greece and the Master of Science (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California. Between 2010-2017 she was working as a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC. She has also been a Lab Associate Intern at Disney Research (summer 2015). Dr Chaspari’s research interests lie in the areas of affective computing, human-computer interaction, signal processing, data science, and machine learning. She is a recipient of the USC Annenberg Graduate Fellowship, USC Women in Science and Engineering Merit Fellowship, and the IEEE Signal Processing Society Travel Grant. Dr. Chaspari has served as a member in various conference organization committees and conference program committees (ACM ACII 2017, IEEE BSN 2018, ACM ICMI 2018).