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Using wearable image sensing to measure physical activity & sedentary behavior

Doherty, Aiden R. (2011) Using wearable image sensing to measure physical activity & sedentary behavior. In: Seminar talk given at University of California, San Diego, 03/03/2011, UCSD.

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This presentation will be of interest to 1) physical activity researchers interested in better capturing participant physical activity and sedentary behaviour, and 2) computer science researchers interested in wearable sensors and image processing challenges in a new application area. In this lecture, Dr Doherty will discuss use of the Microsoft Research SenseCam, a wearable camera sensor which automatically captures up to 5,000 first-person point-of-view images. The images strongly help identify the type of activity a participant is involved in, and can also help determine the environment and situation surrounding the given activity. Because up to 35,000 images can be captured from a participant each week, there exists a substantial information management challenge in storing, annotating, and retrieving image content. This talk will discuss current state-of-art computational approaches applied to SenseCam images, and their application in the field of physical activity.

Item Type:Conference or Workshop Item (Invited Talk)
Event Type:Other
Uncontrolled Keywords:SenseCam, physical activity, lifestyle detection, sedentary behaviour
Subjects:Computer Science > Lifelog
Medical Sciences > Epidemiology
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Microsoft Research, Science Foundation Ireland, British Heart Foundation
ID Code:16294
Deposited On:28 Apr 2011 10:00 by Aiden Doherty. Last Modified 28 Apr 2011 10:00

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