Passive Sensing Analytics and Mobile Health
Passive Sensing Analytics and Mobile Health
September 23, 2021
Speaker: Dr. Nabil Alshurafa, Assistant Professor of Preventive Medicine, Northwestern University
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science
Abstract: Researchers seek to understand human behaviors in their natural setting so they can design interventions that help manage symptoms, prevent illness, and improve health and wellbeing. Wearables (with embedded sensors) combined with machine learning algorithms are increasingly being adopted to understand human behavior. Through analysis of continuous streams of data provided by these sensors, machine learning and analytics pipelines are used to understand a person’s moment-to-moment behavior, psychological state and environmental contexts in which the behavior occurs. This is allowing researchers to understand the interplay between behavior, physiological states and environmental influences along with individual’s physical and mental health. One important goal is to be able to use these novel methods to detect and predict appropriate times to apply interventions that improve health and well-being.
In this talk I hope to go through an overview of the end-to-end process needed for analyzing passive sensing data and inferring human behavior using wearables (with examples in eating and stress). We will go through an example passive sensing data analytic chain (PASDAC), which enables users to clean, curate, segment, classify and evaluate the signals generated from wearable sensors using signal processing and machine learning. We will also touch on current challenges and opportunities for research at the intersection of passive sensing data analytics and mobile health.
Speaker Bio: Nabil Alshurafa is an Assistant Professor of Preventive Medicine and of Computer Science (courtesy) and Electrical and Computer Engineering (courtesy) at Northwestern University. He received his Ph.D. in Computer Science at the University of California Los Angeles (UCLA) in 2015, where his dissertation was awarded the Computer Science outstanding graduating student award, and the Symantec outstanding research award. In 2015, Popular Science magazine highlighted his research on designing a wearable neck-worn sensor WearSens to distinguish between solid and liquid foods consumed. He currently leads the Sensor Analytics program in the Institute for Augmented Intelligence in Medicine and directs the HABits Lab at Northwestern, which aims to bridge between computer science and behavioral science research. His current research seeks to transform our understanding of health constructs by designing objective verifiable wearable sensor measures and machine learning analytic pipelines, to more effectively design interventions that improve lifestyle habits. In 2018, he was awarded a five-year NIDDK NIH Career award, to develop expertise in obesity-related research and advance passive sensing of problematic eating behaviors. He was awarded an NSF EAGER interdisciplinary award, to build privacy conscious technology and analytics that advance our understanding of human behavior. He was also awarded an NIH NIBIB Trailblazer R21 grant for 3 years to study health risk behaviors that involve hand-to-mouth gestures including smoking and overeating. He is currently directing the NIH-funded SenseWhy study, which aims to lay the foundation for studying overeating behaviors among participants with obesity through passive wearable sensors. This study has led to his obtaining an R03 and recently an R01 to validate novel wearable technology for diet and eating assessment in people with obesity. His research has been honored by the National Institute of Nursing Research (NINR) with an Outstanding Poster Abstract Award, Army SBIR with both Phase-I and Phase-II award, and several ACM and IEEE peer-reviewed conferences and journals with a Distinguished Paper Award at ACM IMWUT, a Best Presentation Award at ACM IMWUT, and a Best Poster Award at ACM IMWUT, Best Paper Awards in IEEE Body Sensor Networks (BSN), EAI BODYNETS, The Obesity Society (TOS), and IEEE PerCom (Samsung Best Paper Award).