Monitoring Human Activity
A project of the Artifical Intelligence, Robotics and Vision Laboratory
University of Minnesota, Department of Computer Science and Engineering
Home Detection of Events Real-Time Tracking Action Recognition Learning Patterns from Video Sequences Breathing Abnormality Detection People

Breathing Abnormality Detection

Learning Static Occlusions from a Moving Figure


This module uses LASER rangefinder data to detect abnormalities in patients' breathing patterns in real-time. It detects coughing and hyperventilation, which are characterized by sudden transitions and speeding up of breathing frequency, respectively. Classification of breathing patterns is based on frequency characteristics of the abdominal movement. (3.3MB)

This work is supported by grants from the National Science Foundation, the Minnesota Department of Transportation, the University of Minnesota ITS Institute, and the Department of Homeland Security. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.