Functional Data Methods for Wearable Device Data
Wednesday, October 17, 2018
2:30 PM-3:30 PM
Epidemiology and Biostatistics Seminar Series
In the last ten years, technological advances have made many activity- and physiology-monitoring wearable devices available for use in large-scale epidemiological studies. This trend is likely to continue and even expand, since every smartphone has a built-in accelerometer that can be turned in a physical activity monitoring device. These developments open up a tremendous opportunity for clinical and public health researchers to collect critical data at an unprecedented level of detail, while posing new challenges for statistical analysis of rich, complex data. This talk will present a collection of approaches in functional data analysis for identifying and interpreting variability in activity trajectories within and across participants, for building regression models in which activity trajectories are the response, and for understanding shifts in the circadian rhythms that underlie the timing of activity. We'll draw on several applications, including the Baltimore Longitudinal Study of Aging and data collected through the Columbia Center for Children's Environmental Health.
Jeff Goldsmith is an associate professor in Biostatistics at the Columbia University Mailman School of Public Health. His work is generally focused on functional data analysis, and draws heavily from ongoing collaborations with researchers in other disciplines. One major emphasis is the use of motion kinematics to understand motor control, skill learning, and recovery following stroke; another is physical activity measurement and quantification using wearable devices.
Nancy Colon Anderson