Human Activity Recognition using Physiological Data from Wearables

Project Summary

Traditional Human Activity Recognition (HAR) utilizes accelerometry (movement) data to classify activities. This summer, Team #4 examined using physiological sensors to improve HAR accuracy and generalizability. The team developed ML models that are going to be available open source in the Digital Biomarker Discovery Pipeline (DBDP) to enable other researchers and clinicians to make useful insights in the field of HAR.

 

Project Lead: Jessilyn Dunn

Project Manager: Brinnae Brent

Click here to view the project team's project poster

Watch the team's final presentation (on Zoom) below:

 

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

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Click here to view the project team's poster

 

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View the team's final poster here

Watch the team's final presentation (on Zoom) below: