Human Activity Recognition using Physiological Data from Wearables

Project Summary

Human activity recognition (HAR) is a rapidly expanding field with a variety of applications from biometric authentication to developing home-based rehabilitation for people suffering from traumatic brain injuries. While HAR is traditionally performed using accelerometry data, a team of students led by researchers in the BIG IDEAS Lab will explore HAR with physiological data from wrist wearables. Using deep learning methods, students will extract features from wearable sensor data to classify human activity. The student team will develop a reproducible machine learning model that will be integrated into the Big Ideas Lab Digital Biomarker Discovery Pipeline (DBDP), which is a source of code for researchers and clinicians developing digital biomarkers from wearable sensors and mobile health technologies.

Project Lead: Jessilyn Dunn

Project Manager: Brinnae Brent

Disciplines involved: Health, Biology, Biomedical Engineering

Themes and Categories
Year
2020
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

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