Nabil Alshurafa, Ph.D
Assistant Professor
Preventive Medicine
Presentation Title:
Machine Learning and Mobile Health
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 machine learning and mobile health.