Learning digital features from wearable device data to advance smart health

主讲人：

Peter Song

Dr. Song is Professor of Biostatistics at the University of Michigan School of Public Health, Ann Arbor. He received his PhD in Statistics from the University of British Columbia, Vancouver, Canada in 1996. He has published over 250 peer-reviewed papers and graduated 28 PhD students and trained 6 postdoc research fellows. Dr. Song's current research interests include data integration, distributed inference, highdimensional data analysis, longitudinal data analysis, mediation analysis, and spatiotemporal modeling. He is IMS Fellow, ASA Fellow and Elected Member of the International Statistical Institute. Dr. Song now serves as Area Editor of the Annals of Applied Statistics (Medicine, EHR and Smart Health), Associate Editor of the Journal of American Statistical Association, Journal of the Royal Statistical Society Series B (Statistical Methodology) and the Journal of Multivariate Analysis.

1  报告信息

时间 

2025年12月2日（周二）

10：00

地点 

中国人民大学中关村校区

明德主楼1016

2  报告摘要

Wearable devices are becoming a popular mini robot to collect real-time information of well-being from a device user. Our primary goal is to extract and validate features relevant to personal health conditions from massive high-frequency measurements, so that each device user can utilize personal digital features to guide for personal health management. In this talk, I will introduce several projects that collect wearable devices data to develop cost-effective mobile healthcare programs. Through these diverse public health research projects, we hope to demonstrate the use of such data collected from mobile devices to address some important health-related challenges such as monitoring of infectious disease, sleep health, aging, and reproductive health. I will give an overview on the application of different supervised learning techniques to process wearable devices data and build adaptive digital features to assist people on health-related decision making.