Abstract
We present the results of analyzing gait motion in first-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera as well as validated using the ground truth dataset. Our preliminary results indicate that the extraction of activity from the video in a controlled setting shows strong promise of being utilized in different activity monitoring applications such as in the eldercare environment, as well as for monitoring chronic healthcare conditions.
Original language | English |
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Title of host publication | Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016 |
Editors | Mary Yang, Hamid R. Arabnia, Leonidas Deligiannidis, Leonidas Deligiannidis |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 641-646 |
Number of pages | 6 |
ISBN (Electronic) | 9781509055104 |
DOIs | |
State | Published - Mar 17 2017 |
Event | 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016 - Las Vegas, United States Duration: Dec 15 2016 → Dec 17 2016 |
Conference
Conference | 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016 |
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Country/Territory | United States |
City | Las Vegas |
Period | 12/15/16 → 12/17/16 |
ASJC Scopus Subject Areas
- Computer Science Applications
- Information Systems
- Health Informatics
- Artificial Intelligence
- Computer Networks and Communications
Keywords
- activity detection
- gait analysis
- motion and tracking algorithms and applications
- video analysis