The series hosts a seminar every other week on current research topics. The seminar often features an invited guest speaker and occasionally local faculty members, students or others affiliated with the department. The usual time of the seminar is 3:30-4:30 pm on Fridays. Professors Tatiyana V Apanasovich (email@example.com), Qing Pan (firstname.lastname@example.org) and Emre Barut (email@example.com ) are the Seminar Series Coordinators.
Date: Friday, November 21st, 3:30-4:30pm
Location: Duques Hall, Room 251
Title: Not everybody, but some people move like you: A Biostatistics perspective on wearable computing in public health
Speaker: Ciprian Crainiceanu, Department of Biostatistics, Johns Hopkins University
Abstract: Accelerometers are now used extensively in health studies, where they increasingly replace self-report questionnaires. The sudden success of accelerometers in these studies is due to the fact that they are cheap, easy to wear, collect millions of data points at high frequency (10-100Hz or more), store months worth of data, and can be paired with other devices, such as heart, gps, or skin temperature sensors. I will discuss the multi-resolution structure of the data and will introduce methods for movement recognition both for in-the-lab and in-the-wild data using second- and sub-second level data. I will introduce movelets, a powerful dictionary learning approach, designed for quick identification of movement patterns. At the minute level I will describe activity intensity measures (activity counts, vector magnitude, and activity intensity) and introduce functional data approaches for characterizing the circadian rhythm of activity and its association with health. The natural data structure induced by such observational studies is that of multilevel functional data (activity intensity measured at every minute for multiple days observed within each subject.) I will introduce fast functional data analysis approaches that can deal with the data complexity, describe its structure and its association with health outcomes. In particular, I will discuss results for two motivating studies: 1) the association between age and the circadian rhythm of activity; and 2) the association between mental health disorders and activity patterns.