Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will present, " Biostatistical Methods for Wearable and Implantable Technology."
Wearable and Implantable Technology (WIT) is rapidly changing the Biostatistics data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the signals. In this talk I will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT analytic environment. I will describe the use of WIT sensors including accelerometers, heart monitors, glucose monitors and their combination with ecological momentary assessment (EMA). This rapidly expanding data eco-system is characterized by multivariate densely sampled time series with complex and highly non-stationary structures. I will introduce an array of scientific problems that can be answered using WIT and I will describe methods designed to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data.
Last Updated On Wednesday, March 28, 2018 by Temp, GSPH Marketing & Development
Created On Friday, January 05, 2018
Zeda Li, Paul H. Chook Department of Information Systems and Statistics, City University of New York, will present, “Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series”.
Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT (Wearable and Implantable Technology) analytic environment.
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