Data Science for Health – Biobehavioral Sensing

Dartmouth Events

Data Science for Health – Biobehavioral Sensing

Diabetes is a societal grand challenge that affects 1 in 11 Americans. This condition causes more deaths than AIDS and breast cancer combined.

Friday, February 23, 2018
Kemeny Hall 007
Intended Audience(s): Public
Categories: Lectures & Seminars

Abstract:  Diabetes is a societal grand challenge that affects 1 in 11 Americans. This condition causes more deaths than AIDS and breast cancer combined. Due to the chronic nature of diabetes, consistent ‘patient-centered’ care is paramount for optimum management.  Decades of research support that behavior impacts biology especially in management of diabetes as well as other chronic conditions. However, the ability to understand and quantify biobehavioral factors in daily living is limited. The complexity of this grand challenge calls for interdisciplinary expertise to develop actionable and clinically-relevant solutions while maintaining high usability.  The overarching goal of my research program is to study novel technology-driven solutions that can support and augment clinical practice in disease care and management. In this talk,I will present the role of ubiquitous sensors and associated machine learning algorithms to understand biology-behavior connections and inform personalized healthcare. I will highlight methods that approach this complex problem by monitoring behavior, with an emphasis on
sensor-based dietary monitoring, as well as methods that approach this problem from sensor based biology monitoring. More specifically, I will present data-driven inference from wearable sensors for dietary monitoring in varying free-living environments. Additionally, I will present data-driven inference for improved self-management of diabetes from devices such as continuous glucose monitors and insulin pumps. Biobehavioral understanding in freeliving conditions remains an open problem in its infancy, and thus my research mission is to develop foundational tools to improve the standard of care in chronic diseases like diabetes.
Bio Temiloluwa Prioleau ( is a Rice University Academy Postdoctoral Fellow. She received her M.S. and Ph.D. in Electrical Engineering from Georgia Institute of Technology. Prior to that, she received her B.S. also in Electrical Engineering from the University of Texas at Austin.  Dr. Prioleau’s research is driven by the many complex problems in healthcare that can benefit from engineering and technology-driven solutions. Her research interest lies at the intersection of ubiquitous sensors (mobile and wearable), data analytics, and human health. More specifically, she studies novel sensor-based methods to understand and quantify behavioral biomarkers and inform personalized healthcare in management of chronic conditions such as obesity and diabetes. Dr. Prioleau has been a recipient of the ARCS Foundation Fellowship (2012 - 2016), NSF Graduate Research Fellowship (2013 - 2016), and best paper award at the IEEE Engineering in Medicine and Biology Conference (2014).

For more information, contact:
Sandra Hall

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