ViziCal: accurate energy expenditure prediction for playing exergames

Authors:
Miran Kim;Jeff Angermann;George Bebis;Eelke Folmer
Affiliations:
University of Nevada, Reno, Reno, NV, USA;University of Nevada, Reno, Reno, NV, USA;University of Nevada, Reno, Reno, NV, USA;University of Nevada, Reno, Reno, NV, USA
Venue:
Proceedings of the 26th annual ACM symposium on User interface software and technology
Year:
2013

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Abstract

In recent years, exercise games have been criticized for not being able to engage their players into levels of physical activity that are high enough to yield health benefits. A major challenge in the design of exergames, however, is that it is difficult to assess the amount of physical activity an exergame yields due to limitations of existing techniques to assess energy expenditure of exergaming activities. With recent advances in commercial depth sensing technology to accurately track players' motions in 3D, we present a technique called Vizical that uses a non-linear regression approach to accurately predict energy expenditure in real-time. Vizical may allow for creating exergames that can report energy expenditure while playing, and whose intensity can be adjusted in real-time to stimulate larger health benefits.