Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 143 - New Machine Learning Tools for Mobile Health Data and Individual Intervention
Type: Invited
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Lifetime Data Science Section
Abstract #316980
Title: Supervised Learning of Health-Related Secret Codes from Wearable Devices Data
Author(s): Peter X.K. Song*
Companies: University of Michigan

Wearable devices are becoming a popular mini robot to collect real-time information of well-being from a human device user. The primary goal is to extract and validate relevant features from massive high-frequent measurements, so that each device user can utilize personal secret codes to guide for health self-management. In this talk, I will introduce three smart personal wearables, Empatica E4, ActiGraph GTX and YONO Earbud that we have used in various public health research projects, and demonstrate how data collected from these devices may solve some important health-related questions. I will give an overview on the applications of different supervised learning techniques to process wearable devices data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program